Working Capital Efficiency

and Firm Profitability - A Quantitative Study of Listed

Swedish Firms 2000-2015

Master’s Thesis 30 credits

Department of Business Studies

Uppsala University

Spring Semester of 2017

Date of Submission: 2017-05-30

Oscar Gustén

Tobias Pahkamaa

Supervisor: Jan Lindvall

II

Abstract

This thesis examines the relationship between working capital efficiency and firm

profitability, and how this relationship is affected by economic fluctuations. In the existing

literature, the relationship between working capital efficiency and firm profitability has been

extensively researched. However, the impact of economic fluctuations on the relationship

between working capital efficiency and firm profitability is sparsely researched. To the best of

our knowledge, only Enqvist, Graham and Nikkinen (2014) have addressed the impact of

economic fluctuations on the relationship between working capital efficiency and firm

profitability. This thesis is a replication of their study in another geographical setting, another

time period and studying different types of firms.

Using a sample of 2,589 firm-year observations of listed Swedish firms for the years 2000-

2015, this thesis conducts multiple regression analysis to examine the relationship between

working capital efficiency and firm profitability. The findings of this thesis propose that firms

can enhance profitability by improving their working capital efficiency. However, the

relationship between working capital efficiency and firm profitability does not appear to be

significantly affected by economic fluctuations. This thesis contributes to the existing

literature by further strengthening the understanding of the relationship between working

capital efficiency and firm profitability. In addition, it also adds to the existing literature on

the relationship between working capital efficiency and firm profitability in a Swedish

context.

Keywords: working capital management, working capital efficiency, firm profitability, cash

conversion cycle, days of inventory, days of accounts receivables, days of accounts payables,

economic fluctuations

III

Table of Contents

Abstract ................................................................................................................................................. II List of tables ......................................................................................................................................... IV List of figures ....................................................................................................................................... IV List of abbreviations ............................................................................................................................. V 1. Introduction ....................................................................................................................................... 6 2. Literature review ............................................................................................................................... 8

2.1 Working capital management ........................................................................................................ 8 2.1.1 Strategies of working capital management ............................................................................ 9 2.1.2 Efficiency of working capital management .......................................................................... 11

2.2 Cash conversion cycle ................................................................................................................. 11 2.2.1 Inventory, accounts receivables and accounts payables ...................................................... 12

2.3 Working capital management and economic fluctuations ........................................................... 14 2.4 The original study: Frame, results, discussion and implications ................................................. 15 2.5 Hypotheses formulation .............................................................................................................. 17

3. Methodology..................................................................................................................................... 19 3.1 Replication................................................................................................................................... 19

3.1.1 The original study: Methodological review .......................................................................... 20 3.1.2 Thesis frame ......................................................................................................................... 21

3.2 Operationalization ....................................................................................................................... 22 3.3 Variable selection ........................................................................................................................ 23

3.3.1 Dependent variable .............................................................................................................. 23 3.3.2 Independent variables .......................................................................................................... 24 3.3.3 Dummy variables .................................................................................................................. 25 3.3.4 Control variables .................................................................................................................. 25

3.4 Regression models ....................................................................................................................... 26 3.5 Data ............................................................................................................................................. 27

3.5.1 Population and sample ......................................................................................................... 29 3.5.2 Statistical tools ..................................................................................................................... 30 3.5.3 Descriptive statistics ............................................................................................................. 30 3.5.4 Correlation matrix ................................................................................................................ 32 3.5.5 Data testing .......................................................................................................................... 33

4. Results .............................................................................................................................................. 35 4.1 Regression analysis: Return on assets ......................................................................................... 35 4.2 Regression analysis: Gross operating income ............................................................................. 38 4.3 Hypothesis testing ....................................................................................................................... 40

5. Discussion ......................................................................................................................................... 43 5.1 Working capital efficiency and firm profitability........................................................................ 43

5.1.1 Descriptive statistics and correlations ................................................................................. 47 5.2 Contributions to research ............................................................................................................. 47 5.3 Limitations of this thesis ............................................................................................................. 48 5.4 Replication as a scientific method ............................................................................................... 49

6. Conclusion ........................................................................................................................................ 51 6.1 Suggestions for future research ................................................................................................... 51

References ............................................................................................................................................ 53 Appendices ........................................................................................................................................... 58

Appendix A: Previous research ......................................................................................................... 58 Appendix B: Variable summarization and formulas ......................................................................... 62 Appendix C: Descriptive statistics .................................................................................................... 63 Appendix D: Statistical testing .......................................................................................................... 64

IV

List of tables

Table 1: Previous research on CCC and firm profitability .................................................................... 12 Table 2: Previous research on the individual components of CCC and firm profitability .................... 14 Table 3: Hypotheses group 1 ................................................................................................................. 18 Table 4: Hypotheses group 2 ................................................................................................................. 18 Table 5: Classification of economic downturns and economic booms ................................................. 29 Table 6: Descriptive statistics full sample ............................................................................................. 30 Table 7: Pearson’s correlation matrix.................................................................................................... 32 Table 8: Regression analysis ROA ........................................................................................................ 36 Table 9: Regression analysis GOI ......................................................................................................... 39 Table 10: Hypothesis testing group 1 .................................................................................................... 41 Table 11: Hypothesis testing group 2 .................................................................................................... 42

List of figures

Figure 1: Annual GDP growth (%) of Sweden 2000-2015 ................................................................... 28 Figure 2: Number of observations per year ........................................................................................... 30

V

List of abbreviations

AR Days of accounts receivables

AP Days of accounts payables

CCC Cash conversion cycle

CR Current ratio

DEBT Firm debt ratio

GDP Gross domestic product

GOI

INV

Gross operating income

Days of inventory

OLS Ordinary least squares

ROA Return on assets

SIZE

SMEs

Firm size

Small and medium-sized enterprises

6

1. Introduction

Working capital is a hot and ever-present topic in both business practice and research

(Arvidsson & Engman 2013 p. 9). It is an interesting and important topic because it affects the

operations and strategies of firms (Shin & Soenen 1998). Following the global financial crisis

in 2007-2008, working capital management has gained increased attention in business

practice due to its potential to free capital and cover the liquidity needs of firms (Kaiser &

Young 2009; PwC 2015). In business research, working capital management has been

suggested to impact firm profitability through its impact on firm liquidity (Wang 2002; Eljelly

2004) and a number of studies have concluded that firms can enhance profitability through

efficient management of working capital (e.g. Jose, Lancaster & Stevens 1996; Deloof 2003;

Garcia-Teruel & Martinez-Solano 2007).

The relationship between working capital efficiency and firm profitability has been suggested

to be dependent on country (Koralun-Bereźnicka 2014), industry (Jose et al. 1996) and firm

type (Howorth & Westhead 2003). In addition, working capital management has been

connected to the economic environment in different ways. Merville and Tavis (1973)

discussed how the economic environment affects the constituents of working capital, while

Filbeck and Krueger (2005) more precisely concluded that interest rates, innovation and

competition are the main determinants of working capital over time. Previous studies have

suggested that the working capital of firms is countercyclical (Einarsson & Marquis 2001) and

that firms with higher investments in working capital are hit harder in economic downturns

(Braun & Larrain 2005). However, how the relationship between working capital efficiency

and firm profitability is affected by economic fluctuations is barely researched.

Enqvist, Graham and Nikkinen (2014) studied the impact of business cycles on the

relationship between working capital efficiency and firm profitability among listed Finnish

firms for the years 1990-2008. They concluded that the importance of the relationship

between working capital efficiency and firm profitability varies with the economic

environment and, more specifically, increases in economic downturns. However, there is a

significant gap in the existing literature about the impact of economic fluctuations on the

relationship between working capital efficiency and firm profitability. To fill this gap, this

thesis is a replication study of Enqvist et al. (2014). This adds to the existing literature by

accumulating empirical knowledge to an under-researched field of working capital

management. Enqvist et al. (2014) suggested that their findings are generalizable to the

7

Nordic region as a whole. By drawing upon data from listed Swedish firms for the years

2000-2015, this thesis replicates their study in another geographical setting, another time

period and studying different types of firms. The relationship between working capital

efficiency and firm profitability is sparsely researched in the Swedish context, only addressed

by Yazdanfar and Öhman (2014) in their study of Swedish small and medium-sized

enterprises (SMEs) for the years 2008-2011. This approach will contribute to the

understanding of the relationship between working capital efficiency and firm profitability,

and how this relationship is affected by economic fluctuations in a Swedish context.

Additionally, this thesis contributes to the discussion about the importance of replication

studies in business research.

The aim of this thesis is to examine how the relationship between working capital efficiency

and firm profitability is affected by economic fluctuations among a sample of 2,589 firm-year

observations of listed Swedish firms for the years 2000-2015. Thus, the aim is two-folded.

Firstly, it examines the relationship between working capital efficiency and firm profitability.

In so doing, it contributes to the existing literature by adding findings from listed Swedish

firms. Secondly, it investigates how the aforementioned relationship is affected by economic

fluctuations. This thesis replicates the study of Enqvist et al. (2014) and tests their approach in

another context. The research question of this thesis is formulated:

What is the relationship between working capital efficiency and firm profitability, and

how is this relationship affected by economic fluctuations?

The findings of this thesis suggest that firms can enhance profitability by improving their

working capital efficiency. However, the relationship between working capital efficiency and

firm profitability does not appear to be affected by economic fluctuations among sampled

firms. Therefore, more research is encouraged in order to create an increased understanding of

how the economic environment impacts the relationship between working capital efficiency

and firm profitability.

The remainder of this thesis is structured as follows: in section 2 a review of the literature and

the hypotheses are presented; in section 3 the methodological approach is outlined; in section

4 the results of the statistical data analysis and hypothesis testing are presented; in section 5

the results are discussed, and in section 6 the main conclusions are discussed and suggestions

for future research are presented.

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2. Literature review This thesis is a replication study of Enqvist et al. (2014). Replication studies are conducted

with the purpose to amass a cumulative body of knowledge. Therefore, it is crucial that the

replication study matches the research of the original study to the extent it is possible (Bettis,

Helfat & Shaver 2016). In this thesis, the study of Enqvist et al. (2014) is replicated in another

geographical setting, covering a different time frame and researching different types of firms

with a deeper discussion of the theoretical framework. In this section, a review of the

literature is presented in order to contextualize the research problem and provide the

theoretical background of this research field. Following the discussion of the literature, the

hypotheses of this thesis are formulated.

2.1 Working capital management

Working capital management is a key aspect of capital allocation because it has a direct

impact on the long-term growth opportunities of firms (Arvidsson & Engman 2013 p. 9).

Mauboussin and Callahan (2014) recognize capital allocation as the most fundamental

responsibility of top management. According to them, capital allocation is primarily

concerned with how capital is divided between business operations and claimholders. Within

business operations, capital allocation deals with the division of capital between capital

expenditure, research and development, mergers and acquisitions and working capital. As

such, working capital is a part of a wider business scope. This is an important notion because

working capital is not an isolated issue. By placing working capital in a wider context, its

importance for firms can be better understood.

Working capital is commonly defined as the excess of current assets over current liabilities

(Preve & Sarria-Allende 2010 p. 16). By this definition, working capital management is

concerned with how firms manage their current assets and current liabilities (Arvidsson &

Engman 2013 p. 9). These definitions of working capital and working capital management are

standard definitions in this field of research (e.g. Jose et al. 1996; Deloof 2003). As such,

working capital management has been viewed as all decisions made by management that

affect working capital (Kaur 2010). This means working capital management is a broad

concept including different managerial aspects. In early business research, working capital

was discussed as an analytical tool to analyze the financial position of firms (Guthmann 1934)

and later as an important information source for management (Park 1951). Sagan (1955)

suggested that active management of working capital is a determinant of firm strategy and

9

ultimately of firm performance. Following this discussion, Bierman, Chopra and Thomas

(1975) proposed that working capital serves two purposes. Firstly, working capital is a

financial buffer. This is important because it protects the firm from uncertainty and provides

the means necessary to act on opportunities that arise. Secondly, working capital is a means of

increased earnings. Through investment in working capital, firms can e.g. employ more

generous credit policies which are assumed to increase profits. Filbeck and Krueger (2005)

concluded that working capital management is a determinant of firm strategy and a crucial

success factor of firms. Working capital management has been suggested to be firm-specific

(Merville & Tavis 1973) but more often it has been discussed as dependent on firm type

(Howorth & Westhead 2003), industry-specific (Shin & Soenen 1998) or country-specific

(Koralun-Bereźnicka 2014).

Bierman et al. (1975) proposed working capital management to be concerned with both firm

liquidity and firm profitability. However, others have regarded working capital management

rather as a trade-off between firm liquidity and firm profitability (Raheman & Nasr 2007;

Eljelly 2004). Ebben and Johnson (2011) concluded that efficient utilization of working

capital can improve both firm liquidity and firm profitability. Capital has an opportunity cost

and working capital management is concerned with a balance between the components of

working capital in order to maintain firm operations, while not binding excessive capital in

working capital (Garcia-Teruel & Martinez-Solano 2007). As such, working capital

management is a crucial part of firm operations that ultimately affects firm profitability

through its effects on firm liquidity (Arunkumar & Radharamanan 2011). In recent years, a

growing body of research has connected increased efficiency of working capital management

to improved firm profitability (e.g. Jose et al. 1996; Deloof 2003; Garcia-Teruel & Martinez-

Solano 2007).

2.1.1 Strategies of working capital management

Garcia-Teruel and Martinez-Solano (2007) discuss how firms must consider a balance

between profitability and risk when deciding strategy of working capital management. They

argue that this is because decisions that tend to increase profitability tend to increase the risk

and vice versa. Following their discussion, they suggest that working capital management can

be divided in two principal strategies with direct impact on the working capital of firms.

Aggressive strategies are directed towards the minimization of working capital in order to

maximize profitability. Conservative strategies are accepting higher levels of working capital

10

in order to maintain stable and uninterrupted firm operations. These contrasting strategies will

impact firm operations through different effects on the relationship between risk and

profitability.

Aggressive and conservative strategies of working capital management affect firm operations

in different ways (Jose et al. 1996; Garcia-Teruel & Martinez-Solano 2007). All such

decisions affect the interaction between risk and profitability. More specifically, aggressive

strategies of working capital management actively seek a reduction in working capital in order

to maximize the efficiency of operations. This can be achieved through e.g. lower levels of

inventory holdings (Jose et al. 1996) or tighter credit policies (Garcia-Teruel & Martinez-

Solano 2007). Such decisions free capital for use elsewhere within firms (Arvidsson &

Engman 2013 p. 12) and can be seen as a flexible source of financing that lower the capital

costs of firms (Deloof 2003). It also comes with a potential risk of lower sales due to e.g.

stock-outs (Jose et al. 1996) or customers requiring credits buying elsewhere (Garcia-Teruel

& Martinez-Solano 2007). Conservative strategies of working capital management will accept

higher levels of working capital. This will bind more capital in working capital and increase

the capital cost of firms (Arvidsson & Engman 2013 p. 12). Such decisions will lower the risk

of operations but these benefits are often at the price of lower profitability due to higher

investment in current assets (Garcia-Teruel & Martinez-Solano 2007). Deloof (2003) discuss

that higher levels of working capital can be a result of increased sales or a necessity in order

to meet the demands from customers. He concludes that firm profitability will increase as

long as the costs of increasing sales do not exceed the benefits of the same.

Deloof and Jegers (1996) suggest that firms’ strategy of working capital management will be

affected by their positions in different networks. This has been discussed by Ng, Smith and

Smith (1999) who suggest that the credit policies of firms will affect their long-term

relationships with customers. Further, Deloof (2003) proposes that more profitable firms pay

their suppliers earlier, while Garcia-Teruel and Martinez-Solano (2007) argued that it makes

economic sense to keep capital within firms as long as possible in order to lower the capital

costs of firms. This must be put in relation to firms’ positions in networks and their long-term

relationship with suppliers. These perspectives have often been neglected in previous

research. However, it is possible that the benefits of long-term relationships may counter the

increasing costs of higher investments in working capital.

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2.1.2 Efficiency of working capital management

How firms approach working capital will affect firm strategy through its effects on especially

the management of inventory, accounts receivables and accounts payables (Deloof 2003).

Filbeck and Krueger (2005) proposed that the purpose of the working capital management of

firms is to obtain and maintain the optimum of each of the components of working capital. In

their view, efficient management of inventory, accounts receivables and accounts payables are

crucial in order to reduce external and internal financing costs, and ultimately increase firm

profitability. Traditional measures of the financial health of firms, such as current ratio (CR)

or quick ratio, will focus attention on the wrong things (Hager 1976; Richards & Laughlin

1980). Instead of optimizing working capital, they will encourage firms to maintain higher

levels of inventory and accounts receivables in relation to accounts payables and these must

be financed by debt and equity (Ebben & Johnson 2011). Working capital efficiency is better

captured by the cash conversion cycle (CCC), as it measures the efficiency of firms’

management of inventory, accounts receivables and accounts payables, and not only the

relation between current assets and current liabilities (Gitman 1974).

2.2 Cash conversion cycle

The CCC is a dynamic measure of the ongoing liquidity management of firms that combines

both balance sheet and income statement data, to create a measure with a time dimension

(Jose et al. 1996). The CCC consists of days of inventory (INV), days of accounts receivables

(AR) and days of accounts payables (AP), and captures the time lag between the purchase of

input goods and services to the collection of payment from customers (Arvidsson & Engman

2013 p. 23). As such it is also an operating variable measuring the working capital efficiency

of firms (Garcia-Teruel & Martinez-Solano 2007). While the CCC has been regarded as a

better measurement of firm liquidity than traditional measures (Eljelly 2004), it must be clear

that it has its limitations. Cagle, Campbell and Jones (2013) note that the CCC does not fully

consider current liabilities. As an effect, constituents of current liabilities, such as interest,

payroll and taxes are not considered. These may also have a significant impact on firm

liquidity, and ultimately on firm profitability. Despite this limitation, the CCC is the most

commonly used measure of working capital efficiency in a vast research tradition of working

capital (e.g. Jose et al. 1996; Shin & Soenen 1998; Deloof 2003; Garcia-Teruel & Martinez-

Solano 2007).

12

In this research tradition, a lower CCC is an indicator of higher working capital efficiency. A

vast majority of previous research has found a negative relationship between CCC and firm

profitability (see table 1 below). To the best of our knowledge, there are only two studies

presenting a positive relationship between CCC and firm profitability. Lyroudi and Lazaridis

(2000) examined the relationship between working capital efficiency and firm profitability

among firms in the Greek food industry for the year 1997, and found a positive relationship

between CCC and firm profitability. However, other studies on specific industries have

consistently found a negative relationship between CCC and firm profitability (e.g. Padachi

2006; Ganesan 2007). Sharma and Kumar (2011) studied the effects of working capital

efficiency on firm profitability of Indian firms for the years 2000-2008, and found a positive

relationship between CCC and firm profitability. They suggest that the relationship between

working capital efficiency and firm profitability may be different in emerging markets.

However, other studies in emerging markets have consistently found a negative relationship

between CCC and firm profitability (e.g. Falope & Ajilore 2009; Mathuva 2010). While

Lyroudi and Lazaridis (2000) and Sharma and Kumar (2011) present a positive relationship

between CCC and firm profitability, a large number of studies present a strong case for a

negative relationship between CCC and firm profitability. Previous research is summarized in

table 1 below (for a more detailed presentation of previous research, see appendix A.1).

Table 1: Previous research on CCC and firm profitability

Effect →

Variable

↓

Significant negative relationship with firm

profitability

Significant positive relationship with firm

profitability

CCC Jose, Lancaster & Stevens (1996)

Shin & Soenen (1998)

Wang (2002)

Deloof (2003)

Eljelly (2004)

Lazaridis & Tryfonidis (2006)

Garcia-Teruel & Martinez-Solano (2007)

Falope & Ajilore (2009)

Gill, Biger & Mathur (2010)

Mathuva (2010)

Ebben & Johnson (2011)

Yazdanfar & Öhman (2014)

Lyroudi & Lazaridis (2000)

Sharma & Kumar (2011)

2.2.1 Inventory, accounts receivables and accounts payables

Jose et al. (1996) conceptualize how the strategy of working capital will affect a firm’s CCC.

An aggressive strategy will aim to minimize working capital through shortening of the CCC,

while a conservative strategy will accept higher levels of working capital. Expressed as the

13

function of CCC, an aggressive strategy will aim to achieve lower INV, lower AR and higher

AP in order to minimize the capital bound in working capital and thereby maximizing firm

profitability. A conservative strategy will accept higher levels of working capital in order to

maintain stable and uninterrupted firm operations. This view is problematized by Deloof

(2003) who suggests that a longer CCC might be a consequence of higher sales and thus

increasing profitability. However, if the costs of higher levels of working capital rise faster

than the benefits of e.g. larger inventory holdings or looser credit policies, firm profitability

will be negatively affected by a higher CCC.

Management of inventory, accounts receivables and accounts payables will affect the risk and

profitability of firm operations (Garcia-Teruel & Martinez-Solano 2007). Thus, the

optimization of the components of CCC must affect firm operations. Firstly, INV can either

be kept at a bare minimum or above minimum levels. Lower INV will decrease the cost of

holding inventory and therefore increase the efficiency of operations (Deloof 2003; Enqvist et

al. 2014). However, it will also increase the risk as firms are e.g. risking losing sales due to

stock-outs (Jose et al. 1996; Gill et al. 2010). Secondly, AR can be kept higher or lower.

Lower levels of accounts receivables will decrease the cost of capital and increase firm

efficiency (Deloof 2003; Lazaridis & Tryfonidis 2006). It can also increase the risk because

the firm might e.g. lose customers that require credits (Jose et al. 1996; Sharma & Kumar

2011). Thirdly, AP can be adjusted with different outcomes on the relationship between risk

and efficiency. The longer a firm delays their payments to suppliers, the lower the CCC. This

will decrease the cost of capital, because the capital is kept within the firm for as long as

possible. Higher AP might lead to e.g. negative responses from suppliers (Jose et al. 1996).

However, Deloof (2003) points out that accounts payables can be used as an inexpensive and

flexible source of financing which can be used to increase profitability.

In previous research, increased working capital efficiency has been regarded as lower INV,

lower AR and higher AP. INV and AR have consistently been found to have a negative

relationship with firm profitability. From a theoretical point of view, AP has been assumed to

have a positive relationship with firm profitability. However, previous research has more

often found a negative relationship between AP and firm profitability. Previous research is

summarized in table 2 below (for a more detailed presentation of previous research, see

appendix A.2).

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Table 2: Previous research on the individual components of CCC and firm profitability

Effect →

Variable

↓

Significant negative relationship with firm

profitability

Significant positive relationship with firm

profitability

INV Deloof (2003)

Lazaridis & Tryfonidis (2006)

Garcia-Teruel & Martinez-Solano (2007)

Falope & Ajilore (2009)

Sharma & Kumar (2011)

Enqvist, Graham & Nikkinen (2014)

Mathuva (2010)

AR Deloof (2003)

Lazaridis & Tryfonidis (2006)

Falope & Ajilore (2009)

Gill, Biger & Mathur (2010)

Mathuva (2010)

Sharma & Kumar (2011)

AP Lazaridis & Tryfonidis (2006)

Falope & Ajilore (2009)

Sharma & Kumar (2011)

Enqvist, Graham & Nikkinen (2014)

Mathuva (2010)

2.3 Working capital management and economic fluctuations

Merville and Tavis (1973) suggest that the different components of working capital

management are in interplay with each other as well as with the economic environment.

According to them, working capital must be affected by economic fluctuations. A natural

consequence of this is that the optimization of the individual firm’s working capital

management must take economic fluctuations into consideration. Filbeck and Krueger (2005)

discussed how working capital management changes over time. They concluded that working

capital management change over time because of influences from interest rates, innovation

and competition. In addition, they found that many of the variations in working capital

practices and working capital performance may be caused by economic fluctuations. Different

types of firms are affected differently by economic fluctuations, which lead to different

influences on working capital management.

Einarsson and Marquis (2001) examined the relationship between working capital

management policies and business cycles. They focused on the degree to which firms rely on

external financing to finance working capital over business cycles in the US for the years

1960-1994. Their findings suggest that firms’ external financing of working capital is

countercyclical and that it increases in economic downturns. In line with this, Braun and

Larrain (2005) studied a sample of 57,538 observations from over 100 countries to investigate

15

the link between external financing and growth over business cycles for the years 1963-1999.

Their results suggest that industries that are more dependent on external financing are hit

harder in economic downturns. Industries with higher working capital also relied more on

external financing. An implication of this is that working capital management plays an

important role in the financing of firms, and the way that firms are financed will affect their

performance. Higher working capital efficiency will decrease the need of external financing

and ultimately increase firm profitability.

In previous research, working capital management has been connected to the economic

environment (e.g. Merville & Tavis 1973; Filbeck & Krueger 2005; Enqvist et al. 2014). The

economic environment has been discussed as economic uncertainty over time (Merville &

Tavis 1973) and changes across time due to the factors interest rates, innovation and

competition (Filbeck & Krueger 2005). In addition, Enqvist et al. (2014) discussed business

cycles on the basis of the variation in the annual growth of the gross domestic product (GDP)

in relation to the long-term growth trend of GDP. This is a narrow definition of business

cycles. This thesis is not concerned with understanding the business cycle, but rather

examining the effects of a changing economic environment on the relationship between

working capital efficiency and firm profitability. While there are different economic theories

making different claims regarding the origins and functions of business cycles, in this thesis it

is sufficient to conclude that economic fluctuations have persisted and forms unsystematic but

regular patterns of economic activity (Schön 2013 p. 24). Aggregated economic activity on a

national level is normally measured using GDP. GDP captures the market value of all final

goods and services produced within a nation for a given time period. Economic fluctuations

are normally measured as fluctuations in annual GDP growth (e.g. Neumeyer & Perri 2005;

Enqvist et al. 2014). For this reason, unlike Enqvist et al. (2014), this thesis does not

conceptualize economic fluctuations as business cycles. The same concept is measured in this

thesis but with a different definition in comparison to Enqvist et al. (2014).

2.4 The original study: Frame, results, discussion and implications

Enqvist et al. (2014) examined the role of business cycles, measured as GDP fluctuations, on

the relationship between working capital efficiency and firm profitability (for a

methodological review, see 3.1.1). Firm profitability was measured by return on assets (ROA)

and gross operating income (GOI). Using a sample of 1,136 firm-year observations of listed

Finnish firms for the years 1990-2008, they found a negative relationship between CCC and

16

firm profitability. In addition, the significance of this relationship increased in economic

downturns. Further, they examined the relationship between INV, AR and AP, and firm

profitability. Their findings suggest a significant negative relationship between INV and firm

profitability. This relationship was enhanced in economic downturns. They found a negative

but insignificant relationship between AR and firm profitability. This relationship was

enhanced in economic downturns but unchanged in economic booms. The relationship

between AP and firm profitability was significantly negative, but did not change with

economic fluctuations. Lastly, the relationship between the control variables and firm

profitability was tested. They found a significant positive relationship between CR and firm

profitability. Firm debt ratio (DEBT) was found to have a negative relationship with firm

profitability, however, this relationship was only significant in relation to ROA. Firm size

(SIZE) was found to be negatively related to firm profitability. For the dummy variables, they

found that firm profitability increased during economic booms and decreased during

economic downturns.

Enqvist et al. (2014) suggest there are optimal levels of working capital for firms and that this

optimum will vary with economic fluctuations. This leads them to conclude that quicker

inventory turnover, quicker collection of accounts receivables and shorter cycles of accounts

payables enhance firm profitability. Further, the business environment affects firm

profitability. In economic downturns the profitability of firms decreases, while in economic

booms it increases. In addition, the impact of efficient working capital management increases

in economic downturns because the importance of efficient inventory management and

efficient collection of receivables increases.

A practical business implication of this is that investments in working capital are crucial for

firms and firms should incorporate working capital management in their daily routines.

Enqvist et al. (2014) further suggest their findings have implications for national economic

policy. Firms generate income and employment opportunities, why they are important

functions of the economy. As such, national economic policy has an interest and opportunity

to boost the cash flow of firms. This would increase firms’ ability to finance working capital

internally, especially in economic downturns. Lastly, they suggest that their findings can be

generalized to the Nordic region as a whole.

17

2.5 Hypotheses formulation

There are two groups of hypotheses in this thesis. The first group, hypothesis 1-4, tests the

relationship between working capital efficiency and firm profitability. The second group,

hypothesis 5-8, tests how the relationship between working capital efficiency and firm

profitability is affected by economic fluctuations. Working capital efficiency has in previous

studies been measured using the CCC. The CCC captures the time lag between the purchases

of inputs to the collection of sales. As such, it adds a time dimension that static measurements

do not have. Working capital efficiency has been suggested to shorten the CCC (e.g. Jose et

al. 1996; Shin & Soenen 1998; Deloof 2003). This because a shorter CCC means less capital

is bound in working capital and therefore firms should aim to decrease their CCC through

lower INV, lower AR and higher AP (Arvidsson & Engman 2013 p. 11).

From a theoretical perspective, this is intuitive as less capital bound in working capital frees

capital to be used elsewhere within firms. As a result, firms will be less dependent on external

financing (Filbeck & Krueger 2005) which lower the cost of capital of firms (Deloof 2003).

Through increased working capital efficiency, firms can generate increased internal financing

as a means to e.g. increase profitability (Bierman et al. 1975). In addition, previous research

has found a negative relationship between CCC, INV, AR, AP, and firm profitability. As

such, there is a discrepancy between theory and empirical findings regarding the relationship

between AP and firm profitability. Deloof (2003) argues that more profitable firms pay their

suppliers earlier, while Garcia-Teruel and Martinez-Solano (2007) suggest that higher AP

should lead to increased firm profitability because capital will be kept within firms resulting

in lower capital costs and higher internal financing.

Therefore, in this thesis, there is an expected negative relationship between CCC and firm

profitability, an expected negative relationship between INV and firm profitability, an

expected negative relationship between AR and firm profitability, and an expected positive

relationship between AP and firm profitability. To examine the relationship between working

capital efficiency and firm profitability the same hypotheses as Enqvist et al. (2014) are

tested. The hypotheses are formulated:

18

Table 3: Hypotheses group 1

Hypothesis Prediction

Hypothesis 1 There is a negative relationship between CCC and firm profitability

Hypothesis 2 There is a negative relationship between INV and firm profitability

Hypothesis 3 There is a negative relationship between AR and firm profitability

Hypothesis 4 There is a positive relationship between AP and firm profitability

Working capital management has been discussed to be affected by economic fluctuations

(Einarsson & Marquis 2001; Braun & Larrain 2005). In addition, CCC, INV, AR and AP

have been suggested to depend on the economic environment (Merville & Tavis 1973;

Filbeck & Krueger 2005; Enqvist et al. 2014). An interpretation of this is that firms must take

economic fluctuations in consideration if they want to optimize their working capital

management. Enqvist et al. (2014) suggested that the relationship between working capital

efficiency and firm profitability is more pronounced in economic downturns. To examine the

impact of economic fluctuations on the relationship between working capital efficiency and

firm profitability the same hypotheses as Enqvist et al. (2014) are tested. The hypotheses are

formulated:

Table 4: Hypotheses group 2

Hypothesis Prediction

Hypothesis 5a The significance of the relationship between CCC and firm profitability increases

during economic downturns

Hypothesis 5b The significance of the relationship between CCC and firm profitability decreases

during economic booms

Hypothesis 6a The significance of the relationship between INV and firm profitability increases

during economic downturns

Hypothesis 6b The significance of the relationship between INV and firm profitability decreases

during economic booms

Hypothesis 7a The significance of the relationship between AR and firm profitability increases during

economic downturns

Hypothesis 7b The significance of the relationship between AR and firm profitability decreases during

economic booms

Hypothesis 8a The significance of the relationship between AP and firm profitability increases during

economic downturns

Hypothesis 8b The significance of the relationship between AP and firm profitability decreases during

economic booms

19

3. Methodology

The aim of the thesis is to examine the relationship between working capital efficiency and

firm profitability, and how this relationship is affected by economic fluctuations. In addition,

there is also a methodological discussion in regards to the use of replication in business

research. In this section, the methodological approach of this thesis is presented. Firstly,

replication as a research method is discussed in order to provide a foundation and

understanding for replication as a scientific method as well as motivating the relevance of

replication in this specific case. Secondly, a summarization of the methodology of Enqvist et

al. (2014) is presented. This is a necessity, as it provides the background of the frame of

replication of this thesis. Thirdly, the operationalization of the relationship between working

capital efficiency and firm profitability in regards to economic fluctuations is discussed.

Fourthly, the variables of the study are presented, defined and discussed. The variable

discussion is concluded with a presentation of the regression models. Lastly, the data is

presented and discussed.

3.1 Replication

Replication forms an essential part of the scientific method (Dewald, Thursby & Anderson

1986). While replication is common practice in the natural sciences, there has been a

reluctance to conduct replication studies in the social sciences (Evanschitzky, Baumgarth,

Hubbard & Armstrong 2007). Common arguments against replication studies, within the

social sciences, are that researchers will not be awarded for replicating other researchers’

findings, that researchers conducting replication studies lack imagination or that replication

studies are a reflection of lack of trust in the original study (Dewald et al. 1986; Bryman &

Bell 2015 p. 50). While these are valid concerns, they do not form enough support to discard

replication as a necessary part of the scientific method. Mittelsteadt and Zorn (1984) conclude

that what cannot be replicated is not worth knowing. They mean that both confirmation and

disconfirmations contribute to the existing knowledge. Confirmations increase the

generalizability of the findings of previous studies while disconfirmations propose that new

approaches must be tested.

Bettis et al. (2016) call for more replication studies for reasons based on both business

practice and research. What business practice is concerned, business research studies

oftentimes give implications for business practice. Single empirical studies are bound to the

specific context of the study and cannot establish whether the findings can be generalized to

20

other contexts. Replication, on the other hand, can amass an accumulated body of knowledge

that better supports the implications from business research to business practice. What

business research is concerned, replication in its nature, tests the replicability of prior studies

and if they can be reproduced in different contexts. An interpretation of those arguments holds

that, in order to accumulate knowledge, replication studies are crucial. If studies and findings

are not tested or replicated, there are only one-shot studies, and practitioners and researchers

alike should request more support before basing decisions on untested results (Evanschitzky

et al. 2007).

Evanschitzky et al. (2007) noted that replication studies saw an increasing trend during the

years 1974-1989 but that there was a slow down during the years 1990-2004. As a result, the

editorial policies of leading journals started to encourage more replication studies.

Duvendack, Palmer-Jones and Reed (2015) noted an up going trend in replication studies

from the early 2000s and forwards. However, they conclude that replication studies are still a

small element in the social sciences.

3.1.1 The original study: Methodological review

The study of Enqvist et al. (2014) “The impact of working capital management on firm

profitability in different business cycles: Evidence from Finland” was published in Research

in International Business and Finance. Research in International Business and Finance seek to

highlight the interaction between finance and broader societal concerns. This affects the focus

of the journal and the studies that are published. Research in International Business and

Finance is an American based journal, ranked 458 of 1370 by Scimago Journal & Country

Rank in the category of business, management and accounting (Scimagojr 2017).

Enqvist et al. (2014) used a quantitative method in their study. Using a sample of non-

financial listed Finnish firms, they examined the relationship between working capital

efficiency and firm profitability and how it is affected by business cycles. The data was

collected for the years 1990-2008 from the Research Institute of the Finnish Economy. The

sample consisted of an unbalanced panel of 1,136 firm-year observations. To study the

relationship between working capital efficiency and firm profitability, and how it is affected

by business cycles, multiple regression analysis was conducted. The variables used in the

study were ROA, GOI, CCC, INV, AR, AP, CR, SIZE and DEBT. Working capital efficiency

was operationalized as CCC and its individual components. Firm profitability was

21

operationalized as ROA and GOI respectively. CCC, INV, AR and AP were the independent

variables, while ROA and GOI were proxies of the dependent variable firm profitability. The

control variables in the study were CR, SIZE and DEBT. The regression models were also

controlled for influences of year and industry. Business cycles were captured using changes in

annual GDP growth, and the five years showing the lowest respectively the highest annual

GDP growth forms two different states of the economy used to capture the influences of

business cycles. As such, two dummy variables were included in the study to compare the

different states of the economy with the full time period. D1 captured economic downturns

and D2 captured economic booms. The hypotheses were formulated as in this thesis (see table

3 and table 4) and were tested at the 1%, 5% and 10% significance levels (Enqvist et al.

2014).

3.1.2 Thesis frame

In the original study, Enqvist et al. (2014) draw conclusions with implications for both

business practice and research. However, these findings must be tested further in order to

build a cumulative body of knowledge. With a focus on another geographical setting, another

time frame and different types of firms, this thesis contributes to the understanding of the

relationship between working capital efficiency and firm profitability. While the relationship

between working capital efficiency and firm profitability has been extensively researched,

how this relationship is affected by economic fluctuations is less researched. Therefore, it is

important to further examine the impact of economic fluctuations on the relationship between

working capital efficiency and firm profitability. By replicating the study of Enqvist et al.

(2014), the replicability of their study and the generalizability of their findings can be tested

in another context. This approach adds to the understanding of the relationship between

working capital efficiency and firm profitability and how this relationship is affected by

economic fluctuations.

Bettis et al. (2016) point at the importance to match the research design of the original study

as closely as possible. The reasoning behind is, that it is crucial to calibrate the replication

with the original study. If this cannot be done, it is difficult to build a cumulative body of

knowledge. For this reason, this thesis employs the same method as Enqvist et al. (2014) to

the extent it is possible. The same theoretical starting points are used, the same statistical

approach is conducted and the hypotheses are formulated and tested in the same manner.

22

However, in order to contribute to the cumulative body of knowledge, the replication is done

in another context. The replication is conducted in another geographical setting during another

time period studying different types of firms. In addition, there is a deeper theoretical

discussion of the relationship between working capital efficiency and firm profitability. This

will increase the understanding of how working capital efficiency affects firm profitability in

a broader context, and more specifically, how this relationship is influenced by economic

fluctuations.

3.2 Operationalization

In accordance with the reasoning of Bettis et al. (2016), this thesis tries to match the original

study to the extent it is possible. Therefore, the hypotheses in this thesis are formulated and

tested in the same manner as in the study of Enqvist et al. (2014). Multiple regression analysis

is conducted in order to examine the relationship between working capital efficiency and firm

profitability. Working capital efficiency is measured using the CCC and firm profitability is

measured by ROA and GOI respectively. More specifically, there are two groups of

hypotheses. The first group tests the relationship between working capital efficiency and firm

profitability for the entire time period. In so doing, the CCC and its individual components are

tested against ROA and GOI. The second group tests how this relationship is affected by

economic fluctuations. Economic fluctuations are captured as changes in annual GDP growth

around its long-term trend. Firm profitability is the dependent variable and ROA and GOI are

used as proxies of firm profitability. However, firm profitability may be explained by other

factors. For this reason, control variables are included in the analysis. The control variables

are CR, SIZE and DEBT. In addition, the regression models also control for the influence of

firm, year and industry. The independent variables are CCC, INV, AR and AP and thus

measures working capital efficiency.

In order to examine how the relationship between working capital efficiency and firm

profitability is affected by economic fluctuations, changes in annual GDP growth around its

long-term trend has been used to categorize different states of the economy. The states of the

economy are categorized by the five years showing the lowest respectively the highest annual

GDP growth, and are then compared to the entire time period. The five years showing the

lowest annual GDP growth are referred to as economic downturns. The five years showing the

highest annual GDP growth are referred to as economic booms. D1 is the dummy variable for

23

the conditions of economic downturns and D2 is the dummy variable for the conditions of

economic booms.

3.3 Variable selection

In regression analysis, the relationship between two or more variables is studied in order to

assess whether the variation in one can explain the variation in the other. In multiple

regression analysis, there is more than one independent variable. The independent variables

are the variables that explain the variation in the dependent variable. As such, multiple

regression analysis examines the causal relationship between the dependent variable and the

independent variables. In this section, each of the variables used in this study are presented.

For a summarization of the variables and how they have been defined, see appendix B.

3.3.1 Dependent variable

The dependent variable in this thesis is firm profitability. Firm profitability is measured as

both ROA and GOI. The variation in the dependent variable is assumed to depend on the

variation in the independent variables. ROA has been the most commonly used measure of

firm profitability in the research tradition of working capital efficiency (e.g. Wang 2002;

Garcia-Teruel & Martinez-Solano 2007). In addition, GOI has been used as an alternative

measure of firm profitability (Deloof 2003; Lazaridis & Tryfonidis 2006) or as a

complementary measure in combination with ROA (Enqvist et al. 2014).

3.3.1.1 Return on assets

ROA is an overall indicator of firm profitability (Padachi 2006). As such, ROA avoids capital

structure differences because it does not take into account how the assets of firms are

comprised (Enqvist et al. 2014). In line with the research tradition of working capital

efficiency (e.g. Padachi 2006; Garcia-Teruel & Martinez-Solano 2007; Enqvist et al. 2014),

ROA is calculated as the ratio between net income and total assets.

3.3.1.2 Gross operating income

Deloof (2003) argues that ROA is unfit as a measure of firm profitability in samples

containing firms with high levels of financial assets. Instead he used GOI1 as the measure of

firm profitability. Lazaridis and Tryfonidis (2006) suggested that GOI is a more suitable

1 Gross operating income has been used in the research tradition of working capital efficiency (e.g. Deloof 2003)

and the definition used in this thesis is in line with this research tradition. However, the authors of this thesis are

aware of a potential conceptual confusion as gross operating income has the characteristics of a margin.

24

measure of firm profitability in regards to working capital efficiency. This because GOI is an

operating variable. As such, GOI measures the operational performance of firms (Enqvist et

al. 2014). In line with the research tradition (e.g. Deloof 2003; Lazaridis & Tryfonidis 2006;

Enqvist et al. 2014), GOI is defined as revenue adjusted for cost of goods sold divided by total

assets minus financial assets.

3.3.2 Independent variables

The independent variables in this thesis are used in order to measure working capital

efficiency. Thus, working capital efficiency is operationalized as CCC, INV, AR and AP. The

variation in the independent variables is assumed to explain the variation in firm profitability.

This is the standard approach in previous research (e.g. Jose et al. 1996; Deloof 2003).

3.3.2.1 Cash conversion cycle

Gitman (1974) introduced the CCC as a measure of working capital efficiency. It is a dynamic

measure of working capital efficiency (Jose et al. 1996). In contrast to static measures, it

captures the time lag between the purchases of inputs to the collection of sales of finished

products (Eljelly 2004). The CCC consists of INV, AR and AP and, as such, is a measurement

combining data from both balance sheet and income statement with a time dimension (Jose et

al. 1996). The CCC is measured in days and is calculated as INV plus AR minus AP.

3.3.2.2 Days of inventory

INV measures the average number of days of inventory held by a firm. An interpretation of

this is that it captures how long it takes for a firm to turn its inventory into sales (Jose et al.

1996). Longer INV reflects more capital bound in inventory for a particular level of

operations (Garcia-Teruel & Martinez-Solano 2007). Therefore, lower INV indicates higher

working capital efficiency (Enqvist et al. 2014). INV is calculated as the ratio between

inventory and cost of goods sold multiplied with 365 days.

3.3.2.3 Days of accounts receivables

AR measures the average number of days it takes for a firm to collect payments for sales

outstanding. Higher AR means that more capital is bound in accounts receivables (Jose et al.

1996). Therefore, lower AR indicates higher working capital efficiency (Enqvist et al. 2014).

AR is calculated as the ratio between accounts receivables and revenue multiplied with 365

days.

25

3.3.2.4 Days of accounts payables

AP reflects the average time it takes for firms to pay their suppliers. The higher the AP, the

longer it takes for firms to settle their payment commitments with their suppliers (Garcia-

Teruel & Martinez-Solano 2007). From a theoretical point of view, higher AP is assumed to

enhance firm profitability because capital is kept within the firm and thus lowers the capital

cost (Jose et al. 1996). AP is calculated as the ratio between accounts payables and cost of

goods sold multiplied with 365 days.

3.3.3 Dummy variables

In line with Enqvist et al. (2014), dummy variables are used in order to analyze the impact of

economic fluctuations on the relationship between working capital efficiency and firm

profitability. Two dummy variables are used to distinguish how the relationship between

working capital and firm profitability is affected by economic fluctuations. Economic

fluctuations are measured as changes in annual GDP growth around its long-term trend for the

years 2000-2015. In line with Enqvist et al. (2014), the dummy variables used in this study

capture the five years with the lowest respectively the highest annual GDP growth. As such,

dummy variable 1 (D1) captures the five years with the lowest annual GDP growth and

dummy variable 2 (D2) captures the five years with the highest annual GDP growth.

3.3.4 Control variables

In order to control that the variation in the dependent variable do not depend on factors other

than the independent variables, control variables are included in all regression models. In line

with Enqvist et al. (2014), the control variables used in this study are CR, SIZE and DEBT.

These control variables have been shown to impact firm profitability (Shin & Soenen 1998;

Koralun-Bereźnicka 2014; Enqvist et al. 2014). In addition, the regression models control for

firm, year and industry.

3.3.4.1 Current ratio

Wang (2002) and Eljelly (2004) suggest that working capital management is concerned with

firm liquidity and that firm liquidity ultimately affects firm profitability. In addition, a number

of studies have shown that CR is associated with firm profitability (e.g. Shin & Soenen 1998).

For this reason, CR is included to control for the impact of liquidity on firm profitability. CR

is calculated as the ratio between current assets and current liabilities.

26

3.3.4.2 Firm size

SIZE is one of the most important determinants of working capital (Koralun-Bereźnicka

2014). In previous research in the field of working capital management, revenues or sales has

normally been used to capture SIZE. However, to make it comparable, previous research has

used the natural logarithm of sales (e.g. Deloof 2003; Enqvist et al. 2014). Therefore, in this

thesis SIZE is measured as the natural logarithm of sales.

3.3.4.3 Firm debt ratio

Working capital efficiency has been suggested to both impact and be impacted by the

financing of firms (Filbeck & Krueger 2005). In addition, how firms are financed has been

suggested to affect firm profitability (Braun & Larrain 2005). DEBT measures the relation

between external financing of the firm and its total assets (Lazaridis & Tryfonidis 2006).

DEBT is calculated as the sum of short term loans and long term loans divided by total assets.

3.3.4.4 Firm, year and industry

Using firm-year observations means using panel data in the regression analysis. For this

reason, firm and year effects have been controlled for. In addition, working capital has been

suggested to be dependent on industry (e.g. Jose et al. 1996; Shin & Soenen 1998; Niskanen

& Niskanen 2006). Therefore, industry has been controlled for. Industry was coded in

accordance with the industry categorization of the Stockholm Stock Exchange (Nasdaq 2017).

3.4 Regression models

To examine how the relationship between working capital efficiency and firm profitability

was affected by economic fluctuations, ordinary least squares (OLS) regression analysis was

conducted. The data used in this thesis consists of an unbalanced panel over a 16-year period.

Therefore, the regression models were specified with clustered robust standard errors around

firm ID. The dependent variable was measured as both ROA and GOI. Four independent

variables were tested, why four regression models are used for each firm profitability

measurement. In addition, the regression models are controlled for CR, SIZE and DEBT as

well as for year and industry effects. The regression models are used to assess if the variation

in the independent variables explain the variation in the dependent variable. The regression

models were formulated:

27

Firm profitability = β0 + β1 CCCti + β2 D1 + β3 D2 + β4 (D1*CCCti) + β5 (D2*CCCti) + β6 CRti + β7

SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti

Firm profitability = β0 + β1 INVti + β2 D1 + β3 D2 + β4 (D1*INVti) + β5 (D2*INVti) + β6 CRti + β7

SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti

Firm profitability = β0 + β1 ARti + β2 D1 + β3 D2 + β4 (D1*ARti) + β5 (D2*ARti) + β6 CRti + β7

SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti

Firm profitability = β0 + β1 APti + β2 D1 + β3 D2 + β4 (D1*APti) + β5 (D2*APti) + β6 CRti + β7

SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti

In the regression models above, the dependent variable, firm profitability, is measured by both

return on assets (ROA) and gross operating income (GOI). The constituents of working

capital efficiency, the independent variables, are measured by the cash conversion cycle

(CCC), days of inventory (INV), days of accounts receivables (AR) and days of accounts

payables (AP). The dummy variables are economic downturns (D1) and economic booms

(D2). The control variables are current ratio (CR), the natural logarithm of sales (SIZE) and

firm debt ratio (DEBT) while also controlling for year effects (YEAR) and industry effects

(INDUSTRY). β is coefficient term and ε is the error term.

3.5 Data

The data collection was done through Thomson Reuters Datastream. As such, the data used in

this thesis is secondary. By using secondary data, a satisfying amount of data can be collected

in order to fulfill the aim of the thesis. However, collecting secondary data from a database

has problems that need to be identified and understood in order to conduct a proper analysis

(Bryman & Bell 2015 p. 328-329). Thomson Reuters Datastream contains data from active

firms. This may lead to a survival bias, as the data sample will not include firms that no

longer exist, have been acquired by other firms, have merged with other firms or are no longer

publicly traded on the Stockholm Stock Exchange. An effect of this could be that the sample

is biased towards firms with higher performance. This effect should be greater the farther

back in time the data is collected because the available data is diminishing from year 2015

and downwards. In the regression models, this has been taken into consideration and the

potential impact has been limited through control for firm and year effects. In addition,

secondary data is not necessarily standardized and different accounting practices lead to

different definitions and reporting practices of financial data. This unavoidably leads to a

certain degree of errors in the collected data. For this reason, the data sample has been

28

screened and cleaned from missing values and extreme values. Real GDP data was collected

from Statistics Sweden for the years 2000-2015.

The time period of this thesis is the 16-year period between 2000-2015. The reason behind the

choice of these years is three folded. Firstly, the years 2000-2015 covers three economic

downturns with significant impact on the Swedish annual GDP growth. Secondly, the years

2000-2015 are as recent as possible as 2015 is the latest year with published accounting data

for listed Swedish firms. Thirdly, the survival bias discussed above increases the farther back

in time the data is collected. 2000-2015 is balancing economic fluctuations and the potential

survival bias in a satisfying manner. It provides the opportunity to examine the impact of

economic fluctuations on the relationship between working capital efficiency and firm

profitability, while also minimizing the impact of the survival bias in the database. However,

it must be noted that the firm-year observations diminish gradually from year 2015 down to

year 2000. As such, the data is an unbalanced panel over the 16-year period between 2000-

2015.

Figure 1: Annual GDP growth (%) of Sweden 2000-2015

Source: Statistics Sweden (2017)

The annual GDP growth for the years 2000-2015 is shown in figure 1 above. It provides the

data on which the different states of the economy are based. In line with Enqvist et al. (2014),

the five years with the lowest annual GDP growth are categorized as D1 and the five years

with the highest annual GDP growth are categorized as D2. These dummy variables are then

-6

-4

-2

0

2

4

6

8

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

29

compared to the results for the whole time period 2000-2015. Table 5 below shows how the

categorization of years in D1 and D2 has been done.

Table 5: Classification of economic downturns and economic booms

Economic downturns (D1) Economic booms (D2)

Year Annual GDP

growth (%)

Year Annual GDP

growth (%)

2001 1.6 2000 4.7

2008 -0.6 2004 4.3

2009 -5.2 2006 4.7

2012 -0.3 2010 6.0

2013 1.2 2015 4.1

Source of annual GDP growth (%): Statistics Sweden (2017)

3.5.1 Population and sample

The aim of this thesis is to examine how the relationship between working capital efficiency

and firm profitability is affected by economic fluctuations among listed Swedish firms. Thus,

the target population of the thesis is listed Swedish firms. More precisely, the constituents of

OMXSPI on the Stockholm Stock Exchange. OMXSPI consists of the firms listed on Large

Cap, Mid Cap and Small Cap on the Stockholm Stock Exchange. From Thomson Reuters

Datastream, financial data from the constituent firms of OMXSPI was collected. The

extracted database consisted of 325 yearly observations and a total of 5,200 observations. In

order to conduct proper statistical analysis, the data was screened and cleaned from missing

values and extreme values because these might distort the results (Pevalin & Robson 2009 p.

289). After adjusting for missing data, 2,666 observations remained in the sample. Thereafter,

the remaining sample was screened for outliers and extreme values. The screening was done

for all variables. From the sample 77 observations were excluded because of theoretically

unrealistic values that would have a significant impact on the results. As a result, the sample

used in the statistical analysis consists of 2,589 firm-year observations, which is 49.8% of the

original sample. The remaining sample constitutes an unbalanced panel of 2,589 firm-year

observations. Figure 2 below shows the yearly distribution of observations.

30

Figure 2: Number of observations per year

3.5.2 Statistical tools

The data was prepared and processed in Microsoft Excel 2016 and the statistical data analysis

was conducted in Stata 14. Microsoft Excel was used to prepare the extracted data into a

database usable in Stata. Stata was used to screen and clean the data and to conduct the

statistical data analysis. The statistical data analysis conducted in this study was descriptive

statistics, correlations and multiple regression analysis. In addition, the statistical testing of

the sample and the regression models were done in Stata. Descriptive statistics were used to

show the features and the characteristics of the variables in the sample, correlations were used

to show how the variables in the sample correlate and multiple regression analysis was

conducted to assess the causal relationship between the dependent variable and the

independent variables. Statistical testing was done to show that the data was appropriate to

use in the multiple regression analysis.

3.5.3 Descriptive statistics

Table 6: Descriptive statistics full sample

Variable Mean SD Min Max

CCC 87.485 74.121 -299.582 596.012

INV 69.407 64.739 0 439.379

AR 71.882 41.991 0.839 487.189

AP 53.803 46.049 0 393.451

ROA 0.028 0.164 -1.186 0.767

GOI 0.320 0.312 -1.023 4.662

CR 2.012 1.840 0.143 26.632

SIZE 14.422 2.143 6.921 19.560

DEBT 0.469 0.192 0.007 1.408 Notes: Descriptive statistics for the sampled variables for the years 2000-2015, for a total of number of 2,589 observations.

0

50

100

150

200

250

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

31

Table 6 above presents the descriptive statistics of the variables in the full sample used in the

statistical analysis. For the descriptive statistics of the economic states referred to as economic

downturns and economic booms, see appendix C.

For the period 2000-2015, the data shows an average CCC of 87.5 days for the sampled firms.

In contrast to Enqvist et al. (2014), the average CCC is considerably lower than listed Finnish

firms (108.8 days). In regards to other studies, the average CCC is higher than the findings of

Deloof (2003) among Belgian firms (44.5 days) and the findings of Yazdanfar and Öhman

(2014) among Swedish SMEs (47.3 days). However, the average CCC is considerably lower

than the findings of Jose et al. (1996) among listed US firms (164 days) and the findings of

Lazaridis and Tryfonidis (2006) among listed Greek firms (189 days). The average CCC is

somewhat matching the findings of Garcia-Teruel and Martinez-Solano (2007) among

Spanish SMEs (76.3 days) and the findings of Gill et al. (2010) among listed US firms (90

days).

INV, AR and AP in the sample are 69.4 days, 71.9 days and 53.8 days respectively. The

average INV is considerable lower than the findings of Enqvist et al. (2014) who found an

average INV of 117.6 days. The average AR too differs considerably from the findings of

Enqvist et al. (2014) who found an average AR of 47.6 days. Average AP is similar to the

findings of Enqvist et al. (2014) who found an average AP of 56.4 days. In regards to other

studies in different contexts, the average INV, AR and AP are somewhat matching (e.g.

Garcia-Teruel & Martinez-Solano 2007; Gill et al. 2010).

For the profitability measures, ROA is on average 2.8% and GOI is on average 32% for the

sampled firms for the years 2000-2015. Both profitability measures in this sample are

considerably lower than in the study of Enqvist et al. (2014) who found an average ROA of

8.4% and an average GOI of 101%. In comparison to other studies, the ROA in our sample is

considerably lower (e.g. Garcia-Teruel & Martinez-Solano 2007; Yazdanfar & Öhman 2014).

The average GOI exceeds the findings of Deloof (2003) for listed Belgian firms of 12.2% and

is similar to the findings of Gill et al. (2010) of 30% among listed US firms.

The difference between studies can be derived from the setting of the studies. Different

studies are conducted in different countries, studying different types of firms, different time

periods and using different variables. All these factors have been suggested to affect working

32

capital management and firm profitability. For a more detailed review of previous research,

see appendix A.

3.5.4 Correlation matrix

Table 7: Pearson’s correlation matrix

ROA GOI CCC INV AR AP CR SIZE DEBT

ROA

GOI 0.383***

CCC -0.079*** -0.143***

INV -0.048** 0.079*** 0.707***

AR -0.248*** -0.208*** 0.421*** 0.015

AP -0.166*** 0.152*** -0.231*** 0.281*** 0.255***

CR 0.034** -0.069*** 0.242*** 0.159*** 0.167*** -0.014

SIZE 0.308*** 0.137*** -0.048** 0.028 -0.238*** -0.099*** -0.321***

DEBT -0.097*** 0.0461** -0.199*** -0.172*** -0.075*** 0.010 -0.545*** 0.327***

ROA 0.387*** -0.008 0.037 -0.268*** -0.153*** 0.056 0.284*** -0.081**

GOI 0.319*** -0.130*** 0.082** -0.203*** 0.120*** -0.054 0.107*** 0.036

CCC -0.087** -0.161*** 0.679*** 0.285*** -0.316*** 0.116*** 0.041 -0.148***

INV -0.111*** 0.037* 0.713*** -0.052 0.252*** 0.118*** 0.073** -0.166***

AR -0.222** -0.189*** 0.462*** 0.085** 0.330*** 0.034 -0.199*** -0.036

AP -0.200*** 0.122*** -0.252*** 0.290*** 0.219*** 0.013 -0.122*** -0.029

CR 0.044 -0.081** 0.330*** 0.227*** 0.245*** -0.015 -0.326*** -0.527***

SIZE 0.281*** 0.113*** -0.066* 0.002 -0.234*** -0.089*** -0.309*** 0.329***

DEBT -0.131*** 0.054 -0.233*** -0.219*** -0.082** 0.008 -0.333*** 0.322***

The table shows Pearson’s correlation matrix for the full sample, economic downturns and economic booms. In

the table, the top correlation matrix represents the full sample, the middle represents economic booms and the

bottom represents economic downturns. ***. Significant at the 0.01 level, ** Significant at the 0.05 level, *.

Significant at the 0.1 level. Full sample observations: 2,589, economic downturns: 847 and economic booms:

779.

Table 7 above presents the correlation matrix of the variables. For the full sample, CCC has a

negative significant correlation with both ROA (-0.079***) and GOI (-0.143***). During

economic downturns, CCC has a significant negative correlation with both ROA (-0.087**)

and GOI (-0.161***). During economic booms, CCC has an insignificant negative correlation

with ROA (-0.008) and a significant negative correlation with GOI (-0.130***). Thus, there

are slight differences between the full sample and the different economic states. However,

there are indications that the correlation is stronger in economic downturns, than in the full

sample and economic booms. In addition, the correlation is stronger between CCC and GOI

than between CCC and ROA.

33

For the full sample, INV has a significant negative correlation with ROA (-0.048***) and a

significant positive correlation with GOI (0.079***). During economic downturns, INV has a

significant negative correlation with ROA (-0.111***) but a positive correlation with GOI

(0.037*). During economic booms, INV has an insignificant positive correlation with ROA

(0.037) and a significant positive correlation with GOI (0.082**). Thus, there is varying and

low correlations between INV and both ROA and GOI. There are indications that the

correlation between INV and firm profitability is stronger in economic downturns when firm

profitability is measured as ROA. However, this is not the case for GOI. The correlations

between INV and both ROA and GOI are varying and low.

For the full sample, AR has a significant negative correlation with both ROA (-0.248***) and

GOI (-0.208***). During economic downturns, AR has a significant negative correlation with

ROA (-0.222**) and a significant negative correlation with GOI (-0.189***). During

economic booms, AR has a significant negative correlation with ROA (-0.268***) and a

significant negative correlation with GOI (-0.203***). Thus, there is a consistently moderate

negative correlation between days of AR and both ROA and GOI. However, the negative

correlations are stronger in economic booms than in economic downturns.

For the full sample, AP has a significant negative correlation with ROA (-0.166***) but a

significant positive correlation with GOI (0.152***). During economic downturns, AP has a

significant negative correlation with ROA (-0.200***) but a significant positive correlation

with GOI (0.122***). During economic booms, AP has a significant negative correlation with

ROA (-0.153***) but a significant positive correlation with GOI (0.120***). Thus, there is a

difference between how AP correlates with the ROA and GOI. ROA has a consistently

negative relationship and GOI has a consistently positive relationship. The correlation is

stronger in economic downturns than for the full sample when firm profitability is measured

as ROA, but not when measured as GOI.

3.5.5 Data testing

In order to conduct OLS regression, the sample needs to fulfill certain criteria (Pevalin &

Robson 2009 p. 288-289). Firstly, the sample size needs to be large enough in order to make

the regression replicable and thus the results generalizable. The sample size in this thesis is in

line with previous research in the research field (e.g. Deloof 2003; Enqvist et al. 2014).

Secondly, OLS regression is sensitive towards outliers and extreme values. These have been

34

checked and removed from the sample (see appendix D.1). However, in this thesis we have

chosen to remove as few observations as possible. The reason for this is that there is a

discrepancy between reality and stylized theoretical examples. While some observations may

be interfering statistically, they are still actual outcomes. This lowers the strength of the

statistical analysis but is a conscious choice. Thirdly, OLS regression requires that the

association between the independent variables and the dependent variable is linear. The

variables have been tested for linearity and this criterion is fulfilled (see appendix D.2).

Fourthly, OLS regression requires that the residuals are normally distributed. Normal

distribution has been checked for and the residuals are normally distributed in all regression

models (see appendix D.3). Fifthly, OLS regression requires that the variance of the residuals

is constant. This has been checked for and the variance of the residuals show

homoscedasticity which means that the variance of the residuals is constant (see appendix

D.4). Lastly, OLS regression requires that the independent variables are tested for

multicollinearity. This assumption has been tested through the VIF-test and multicollinearity

does not impact the regression models (see appendix D.5). Using OLS regression on panel

data requires that there is no autocorrelation between firm-year observations. This has been

controlled for in all regression models by clustering the regression models around robust

standard errors for firm ID and controlling for year effects. To test the robustness of the

regression models, several robustness checks were conducted. In line with Garcia-Teruel and

Martinez-Solano (2007) endogeneity was identified as a potentially distorting effect. This

effect was minimized by including control variables and also measuring firm profitability

using two different profitability measurements. In addition, the regression models were run

with and without control variables and showed consistent results with the regression models

used in the final statistical data analysis. This increases the robustness of the regression

models and also minimized the risk of overfitting in the regression models.

35

4. Results

In this section, the results of the regression analysis and the hypothesis testing are presented.

Firstly, the result of the regression analysis with ROA as the proxy of firm profitability is

presented. Secondly, the result of the regression analysis with GOI as the proxy of firm

profitability is presented. Working capital efficiency is measured as CCC and its individual

components. Lastly, the results of the hypothesis testing are presented.

4.1 Regression analysis: Return on assets

The impact of working capital efficiency on ROA in different economic states was analyzed

to assess how the relationship between working capital efficiency and ROA was affected in

the different economic states. Below are the regression models for ROA:

Model (CCC): ROA = β0 + β1 CCCti + β2 D1 + β3 D2 + β4 (D1*CCCti) + β5 (D2*CCCti) + β6 CRti +

β7 SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti

Model (INV): ROA = β0 + β1 INVti + β2 D1 + β3 D2 + β4 (D1*INVti) + β5 (D2*INVti) + β6 CRti +

β7 SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti

Model (AR): ROA = β0 + β1 ARti + β2 D1 + β3 D2 + β4 (D1*ARti) + β5 (D2*ARti) + β6 CRti + β7

SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti

Model (AP): ROA = β0 + β1 APti + β2 D1 + β3 D2 + β4 (D1*APti) + β5 (D2*APti) + β6 CRti + β7

SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti

Table 8 below, presents the results of the regression analysis for ROA. The explanatory levels

(R-square) of the regression models are somewhat similar as in the study of Enqvist et al.

(2014). Model (CCC) examines the relationship between CCC and ROA and the results show

that Model (CCC) explains 20% of the variation in ROA. The results show a significant

negative relationship between CCC and ROA. This result is in line with the result of Enqvist

et al. (2014). At first sight, this result may seem low. However, it means that a decrease of one

day of CCC would lead to an increase in ROA of 0.037%. This is a considerable increase of

ROA. The strength of the relationship between CCC and ROA decreases significantly in both

economic downturns (D1*CCC) and economic booms (D2*CCC) in relation to the full

sample. In addition, there are no indications that there are any differences between economic

downturns (D1*CCC) and economic booms (D2*CCC). ROA is more pronounced in D2 in

relation to D1.

36

Table 8: Regression analysis ROA

Dependent: ROA

Coefficient

estimator

Expected

Sign

Model

(CCC)

Model

(INV)

Model

(AR)

Model

(AP)

Constant -0.34970***

(0.03612)

-0.35543***

(0.03783)

-0.27202***

(0.0353)

-0.31206***

(0.04534)

D1 - -0.02679*

(0.01585)

-0.00014

(0.01448)

-0.03608*

(0.01808)

-0.01864

(0.0162)

D2 + -0.05583*

(0.02803)

-0.05999*

(0.02591)

-0.02832

(0.02628)

-0.04895*

(0.02384)

CCC - -0.00037***

(0.00008)

(D1*CCC) - 0.00005

(0.00011)

(D2*CCC) + 0.00015

(0.00012)

INV - -0.0002*

(0.0002)

(D1*INV) - -0.00014

(0.00014)

(D2*INV) + 0.0002

(0.00013)

AR - -0.00072***

(0.00018)

(D1*AR) - 0.00017

(0.00022)

(D2*AR) + -0.00009

(0.00025)

AP + -0.00037*

(0.00022)

(D1*AP) + -0.00006

(0.00024)

(D2*AP) - 0.00006

(0.0003)

CR 0.00848***

(0.00233)

0.00682**

(0.00241)

0.00797**

(0.00229)

0.00574*

(0.0024)

SIZE 0.02993***

(0.00231)

0.0306***

(0.00237)

0.02725***

(0.00216)

0.0298***

(0.00239)

DEBT -0.16443***

(0.02133)

-0.16351***

(0.02203)

-0.14505***

(0.02164)

-0.15428***

(0.02174)

R-square 0.20 0.19 0.21 0.18

F-value 10.32 9.44 12.34 13.18

N 2,589 2,589 2,589 2,589

Year Included Included Included Included

Industry ID Included Included Included Included

Clustered around

Firm ID

Yes Yes Yes Yes

The table reports the result of the estimated regression models when firm profitability is measured as ROA. D1

and D2 are dummy variables for economic downturns respectively economic booms; CCC is cash conversion

cycle; (D1*independent) and (D2*independent) are interaction variables; INV is days of inventory; AR is days

of accounts receivables; AP is days of accounts payables; CR is current ratio; SIZE; is natural logarithm of sales;

DEBT is firm debt ratio. Year and industry ID have been controlled. The regression models are clustered around

firm ID. ()- Standard deviation, ***. Significant at the 0.01 level, ** Significant at the 0.05 level, *. Significant

at the 0.1 level.

Model (INV) examines the relationship between INV and ROA and the results show that

Model (INV) explains 19% of the variation in ROA. The results show a significant negative

relationship between INV and ROA. This result is in line with Enqvist et al. (2014). The

37

strength of the relationship between INV and ROA decreases significantly in both economic

downturns (D1*INV) and economic booms (D2*INV) in relation to the full sample. However,

there are indications that the relationship is stronger in economic downturns (D1*INV) than in

economic booms (D2*INV). ROA is more pronounced in D2 in relation to D1.

Model (AR) examines the relationship between AR and ROA and the results show that Model

(AR) explains 21% of the variation in ROA. The results show a significant negative

relationship between AR and ROA. This result is in line with Enqvist et al. (2014). However,

Enqvist et al. (2014) do not find a significant relationship between AR and ROA. The strength

of the relationship between AR and ROA decreases significantly in both economic downturns

(D1*AR) and economic booms (D2*AR) in relation to the full sample. In addition, there are

no significant differences between economic downturns (D1*AR) and economic booms

(D2*AR). However, there are indications that the relationship between AR and ROA is

stronger in economic booms (D2*AR) than in economic downturns (D1*AR). ROA is more

pronounced in D1 in relation to D2.

Model (AP) examines the relationship between AP and ROA and the results show that Model

(AP) explains 18% of the variation in ROA. The results show a significant negative

relationship between AP and ROA. This result is in line with Enqvist et al. (2014). However,

Enqvist et al. (2014) do not have a significant relationship between AP and ROA. The

strength of the relationship between AP and ROA decreases significantly in both economic

downturns (D1*AP) and economic booms (D2*AP) in relation to the full sample. In addition,

there are no significant differences between economic downturns (D1*AP) and economic

booms (D2*AP). However, there are indications that the relationship between AP and ROA is

stronger in economic booms (D2*AP) than in economic downturns (D1*AP). ROA is more

pronounced in D2 in relation to D1.

The regression models for ROA as a proxy of firm profitability are influenced by the control

variables. There is a significant positive relationship between CR and ROA in all models, this

indicates that firms can increase ROA by increasing the margin of liquidity. In addition, there

is also a significant positive relationship between SIZE and ROA. Between DEBT and ROA

there is a significant negative relationship, this indicates that higher leverage of firms

negatively affects ROA.

38

4.2 Regression analysis: Gross operating income

The impact of working capital efficiency on GOI in different economic states was analyzed to

assess how the relationship between working capital efficiency and GOI were affected in the

different economic states. Below are the regression models for GOI:

Model (CCC): GOI = β0 + β1 CCCti + β2 D1 + β3 D2 + β4 (D1*CCCti) + β5 (D2*CCCti) + β6 CRti +

β7 SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti

Model (INV): GOI = β0 + β1 INVti + β2 D1 + β3 D2 + β4 (D1*INVti) + β5 (D2*INVti) + β6 CRti + β7

SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti

Model (AR): GOI = β0 + β1 ARti + β2 D1 + β3 D2 + β4 (D1*ARti) + β5 (D2*ARti) + β6 CRti + β7

SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti

Model (AP): GOI = β0 + β1 APti + β2 D1 + β3 D2 + β4 (D1*APti) + β5 (D2*APti) + β6 CRti + β7

SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti

Table 9 below, presents the results of the regression models for GOI. The explanatory levels

(R-square) of the regression models are lower than in the study of Enqvist et al. (2014).

Model (CCC) examines the relationship between CCC and GOI and the results show that

Model (CCC) explains 8% of the variation in GOI. The results show a significant negative

relationship between CCC and GOI. This result is in line with the result of Enqvist et al.

(2014). The strength of the relationship between CCC and GOI decreases significantly in both

economic downturns (D1*CCC) and economic booms (D2*CCC) in relation to the full

sample. In addition, there are no indications that there are any differences between economic

downturns (D1*CCC) and economic booms (D2*CCC). GOI is more pronounced in D2 in

relation to D1.

Model (INV) examines the relationship between INV and GOI and the results show that

Model (INV) explains 7% of the variation in GOI. The results show an insignificant positive

relationship between INV and GOI. This result is in contradiction with Enqvist et al. (2014).

The strength of the relationship between INV and GOI increases in both economic downturns

(D1*INV) and economic booms (D2*INV) in relation to the full sample. However, the results

are insignificant. In addition, there are indications that the relationship is stronger in economic

downturns (D1*INV) than in economic booms (D2*INV). GOI is more pronounced in D2 in

relation to D1.

39

Table 9: Regression analysis GOI

Dependent: GOI

Coefficient

estimator

Expected

Sign

Model

(CCC)

Model

(INV)

Model

(AR)

Model

(AP)

Constant 0.23343***

(0.06312)

0.19752**

(0.06456)

0.45605***

(0.06377)

0.11769

(0.07726)

D1 - -0.01761

(0.04612)

-0.00583

(0.04317)

-0.02734

(0.04325)

-0.01875

(0.03417)

D2 + -0.09213*

(0.03985)

-0.11009*

(0.03857)

-0.04024

(0.04381)

-0.1036*

(0.03566)

CCC - -0.00051**

(0.00016)

(D1*CCC) - -0.00003

(0.00022)

(D2*CCC) + -0.00012

(0.00022)

INV - 0.00019

(0.00019)

(D1*INV) - -0.00019

(0.00023)

(D2*INV) + -0.00002

(0.00025)

AR - -0.0017***

(0.00024)

(D1*AR) - 0.00006

(0.00032)

(D2*AR) + -0.00055

(0.00037)

AP + 0.00061**

(0.00035)

(D1*AP) + -0.00001

(0.00028)

(D2*AP) - -0.00015

(0.00032)

CR -0.00006

(0.00338)

-0.00468

(0.0033)

0.00114

(0.00344)

-0.00398

(0.00333)

SIZE 0.01962***

(0.04621)

0.01919***

(0.00462)

0.0124**

(0.00433)

0.019923***

(0.00463)

DEBT -0.05579

(0.04725)

-0.02738

(0.04771)

-0.01419

(0.04752)

-0.0312

(0.04758)

R-square 0.08 0.07 0.12 0.07

F-value 6.29 7.01 10.16 6.66

N 2,589 2,589 2,589 2,589

Year Included Included Included Included

Industry ID Included Included Included Included

Clustered around

Firm ID

Yes Yes Yes Yes

The table reports the result of the estimated regression models when firm profitability is measured as GOI. D1

and D2 are dummy variables for economic downturns respectively economic booms; CCC is cash conversion

cycle; (D1*independent) and (D2*independent) are interaction variables; INV is days of inventory; AR is days

of accounts receivables; AP is days of accounts payables; CR is current ratio; SIZE; is natural logarithm of sales;

DEBT is firm debt ratio. Year and industry ID have been controlled. The regression models are clustered around

firm ID. ()- Standard deviation, ***. Significant at the 0.01 level, ** Significant at the 0.05 level, *. Significant

at the 0.1 level.

Model (AR) examines the relationship between AR and GOI and the results show that Model

(AR) explains 12% of the variation in GOI. The results show a significant negative

relationship between AR and GOI. This result is in line with Enqvist et al. (2014). However,

40

the strength of the relationship between AR and GOI decreases in both economic downturns

(D1*AR) and economic booms (D2*AR) in relation to the full sample. There are no

significant differences between economic downturns (D1*AR) and economic booms

(D2*AR). There are indications that the relationship is stronger in economic booms (D2*AR)

than in economic downturns (D1*AR). GOI is more pronounced in D2 in relation to D1.

Model (AP) examines the relationship between AP and GOI and the results show that Model

(AP) explains 7% of the variation in GOI. The results show a significant positive relationship

between AP and GOI. This result is in contradiction with Enqvist et al. (2014). However, the

strength of the relationship between AP and GOI decreases in both economic downturns

(D1*AR) and economic booms (D2*AP). There are no significant differences between

economic downturns (D1*AP) and economic booms (D2*AP). There are indications that the

relationship is stronger in economic booms (D2*AP) than in economic downturns (D1*AP).

GOI is more pronounced in D2 in relation to D1.

The regression models for GOI as a proxy of firm profitability are influenced by the control

variables. There is no significant relationship between CR and GOI in the models, which

indicates that the margin of liquidity does not affect GOI. Between SIZE and GOI there is a

significant positive relationship. There is a significant negative relationship between DEBT

and GOI, this indicates that higher leverage of firms negatively affects GOI.

4.3 Hypothesis testing

The hypotheses in this thesis are divided in two groups. The first group, hypothesis 1-4, tests

the relationship between working capital efficiency and firm profitability for the full sample.

Consequently, these hypotheses test the relationship between working capital efficiency and

firm profitability. The second group, hypothesis 5-8, tests how the aforementioned

relationship is affected by economic fluctuations. More specifically, hypothesis 1-4, tests how

CCC, INV, AR and AP affect ROA and GOI respectively. Hypothesis 5-8, tests how those

relationships are affected in relation to D1 and D2. In line with previous research, the

hypotheses are tested on the 1%, 5% and 10% significance levels (e.g. Deloof 2003; Enqvist

et al. 2014). The results differ between the two proxies of firm profitability, why the

hypothesis testing has been presented for both ROA and GOI individually. This is in

contradiction to Enqvist et al. (2014) who did a weighting of ROA and GOI to answer for

firm profitability, despise varying results for ROA and GOI.

41

In table 10 below, the results of the hypothesis testing are presented for hypothesis 1-4.

Hypothesis 1-4 have different results depending on the measurement of firm profitability.

When firm profitability is measured as ROA, hypotheses 1, 2 and 3 are supported while

hypothesis 4 is rejected. When firm profitability is measured as GOI, hypotheses 1, 3 and 4

are supported while hypothesis 2 is rejected. In contrast to Enqvist et al. (2014), hypotheses

with inconclusive results have been presented if ROA and GOI show contradicting results.

Table 10: Hypothesis testing group 1

Hypothesis Prediction Result Enqvist et al. (2014)

Result

ROA GOI Overall ROA GOI Overall

Hypothesis

1

There is a negative

relationship between CCC

and firm profitability

Support Support Support Support Support Support

Hypothesis

2

There is a negative

relationship between INV

and firm profitability

Support Reject Inconclusive Support Support Support

Hypothesis

3

There is a negative

relationship between AR

and firm profitability

Support Support Support Reject Reject Reject

Hypothesis

4

There is a positive

relationship between AP

and firm profitability

Reject Support Inconclusive Reject Reject Reject

In table 11 below, the results of the hypothesis testing are presented for hypothesis 5-8. The

results for hypothesis testing of hypothesis 5-8 do not differ in regards to the measurement of

firm profitability. There is no significant evidence supporting hypothesis 5-8. Therefore,

hypothesis 5-8 are all rejected. An interpretation of this is that relationship between working

capital efficiency and firm profitability is not affected by economic fluctuations among

sampled firms. This is in contrast to Enqvist et al. (2014), who found support for hypotheses

5a, 6a and 7a.

42

Table 11: Hypothesis testing group 2

Hypothesis Prediction Result Enqvist et al. (2014)

Result

ROA GOI Overall ROA GOI Overall

Hypothesis

5a

The significance of the relationship

between CCC and firm profitability

increases during economic

downturns

Reject Reject Reject Reject Support Support

Hypothesis

5b

The significance of the relationship

between CCC and firm profitability

decreases during economic booms

Reject Reject Reject Reject Reject Reject

Hypothesis

6a

The significance of the relationship

between INV and firm profitability

increases during economic

downturns

Reject Reject Reject Reject Support

Support

Hypothesis

6b

The significance of the relationship

between INV and firm profitability

decreases during economic booms

Reject Reject Reject Reject Reject Reject

Hypothesis

7a

The significance of the relationship

between AR and firm profitability

increases during economic

downturns

Reject Reject Reject Reject Support Support

Hypothesis

7b

The significance of the relationship

between AR and firm profitability

decreases during economic booms

Reject Reject Reject Reject Reject Reject

Hypothesis

8a

The significance of the relationship

between AP and firm profitability

increases during economic

downturns

Reject Reject Reject Reject Reject Reject

Hypothesis

8b

The significance of the relationship

between AP and firm profitability

decreases during economic booms

Reject Reject Reject Reject Reject Reject

43

5. Discussion

The findings of this thesis are broadly consistent with the findings of Enqvist et al. (2014) for

hypotheses group 1. However, for hypotheses group 2 there is a discrepancy between the

findings of this thesis and the original study. In this section, the findings of this thesis will be

discussed in relation to Enqvist et al. (2014) and to the broader context of previous research.

In addition, the limitations of this study will be discussed as well as replication as a scientific

method in business research.

5.1 Working capital efficiency and firm profitability

In this thesis two groups of hypotheses have been tested. The first group, hypothesis 1-4, tests

the general relationship between working capital efficiency and firm profitability. The second

group, hypothesis 5-8, tests how this relationship is affected by economic fluctuations.

Working capital efficiency is measured as CCC and its individual components. Firm

profitability is measured as ROA and GOI respectively. While the coefficient in the

regression analysis may seem low, it is somewhat matching the findings of previous studies

(e.g. Jose et al. 1996; Deloof 2003). Interpreting the results, a decrease in working capital is

suggested to have a significant positive impact on firm profitability. It is noteworthy that the

results of the regression analysis vary with the proxy of firm profitability. This was also found

in the study of Enqvist et al. (2014).

ROA has been the most commonly used measure of firm profitability (e.g. Jose et al. 1996;

Lyroudi & Lazaridis 2000; Wang 2002; Garcia-Teruel & Martinez-Solano 2007; Sharma &

Kumar 2011; Yazdanfar & Öhman 2014) but the suitability of ROA in regards to working

capital management has been discussed by others (Deloof 2003; Lazaridis & Tryfonidis

2006). Derived from this discussion, a number of studies have used GOI to measure firm

profitability (Deloof 2003; Lazaridis & Tryfonidis 2006; Gill et al. 2010; Enqvist et al. 2014).

According to Deloof (2003), the operating activities of firms with high levels of financial

assets will contribute little to ROA. Therefore, he suggests GOI as a measure firm

profitability as it captures the operational performance of firms. Lazaridis and Tryfonidis

(2006) too highlight the importance to connect working capital management to an operating

measure of firm profitability. This debate is important, because as shown in this thesis, and by

Enqvist et al. (2014), the result will vary depending on the measure of firm profitability. An

implication of this is that the findings of previous research depend on how firm profitability

has been measured.

44

Hypothesis 1 examines the relationship between CCC and firm profitability. The findings

provide support for hypothesis 1 when tested against both ROA and GOI. This indicates that

there is a negative relationship between CCC and firm profitability, which means that firms

can enhance profitability through shortening of their CCC. This is in line with Enqvist et al.

(2014) and corresponds to the overall findings in previous studies (see appendix A.1).

Hypothesis 2 examines the relationship between INV and firm profitability. The findings of

this thesis are inconclusive when weighting ROA and GOI together. This is in contradiction to

Enqvist et al. (2014) who found support for hypothesis 2. However, when looking at the

profitability measures individually, there is support for ROA but not for GOI. As such, there

are indications that there is a negative relationship between INV and firm profitability. This is

in line with the findings of previous studies and indicates that firms can increase profitability

by lowering INV (Deloof 2003; Lazaridis & Tryfonidis 2006; Garcia-Teruel & Martinez-

Solano 2007; Sharma & Kumar 2011). It also highlights the aforementioned discussion

regarding the influence of the choice of profitability measure.

Hypothesis 3 examines the relationship between AR and firm profitability. The findings

support hypothesis 3. This too contradicts the findings of Enqvist et al. (2014). While Enqvist

et al. (2014) rejected hypothesis 3, they did find a negative but insignificant relationship

between AR and GOI. Adding the findings of Deloof (2003) and Lazaridis and Tryfonidis

(2006) there are indications that firms can enhance their profitability by lowering AR. While

there is a possibility for firms to increase profitability by reducing AR, this must be weighed

against the effects this could have on long-term relationships with customers (Ng et al. 1999).

Therefore, it is not surprising that previous research has largely failed to find support for this

position.

Hypothesis 4 examines the relationship between AP and firm profitability. The findings of

this thesis are inconclusive when weighting ROA and GOI together. Enqvist et al. (2014)

rejects hypothesis 4 for both profitability measures. When looking at ROA and GOI

individually, there is a significant negative relationship between AP and ROA and a

significant positive relationship between AP and GOI. ROA and GOI differ in their structure

and in how firm profitability is measured. This highlights the aforementioned discussion that

the choice of variables will affect the results. Which hypotheses are supported or rejected will,

in a broader perspective, have a potential impact on the formulation of theory. However, in

45

previous research the impact of AP on firm profitability has been found both negative and

positive. This may seem counterintuitive, but has been discussed by both Deloof (2003) and

Garcia-Teruel and Martinez-Solano (2007). Garcia-Teruel and Martinez-Solano (2007)

discuss that it makes economic sense to assume that higher AP will increase firm profitability

because capital will be kept within firms resulting in lower capital costs and higher internal

financing. However, Deloof (2003) discusses that more profitable firms pay their suppliers

earlier. Excessive capital bound in AP might have a negative effect on firm profitability, but

this must be weighed against e.g. relationships with suppliers (Jose et al. 1996). For this

reason, it is also understandable that there is no consensus in previous research regarding the

relationship between AP and firm profitability. It is an interesting finding. From a theoretical

point of view, higher AP should enhance firm profitability because capital is kept within the

firm and can be used elsewhere within the firm (Garcia-Teruel & Martinez-Solano 2007). The

result in this thesis points in this direction when firm profitability is measured as GOI. On the

other hand, previous research has more often found a negative relationship between AP and

firm profitability. Deloof (2003) argued that more profitable firms pay their suppliers earlier.

The result in this thesis points in this direction when firm profitability is measured as ROA.

Hypothesis 5-8 have all been rejected in this thesis because no statistically significant results

were found. This is in contradiction to Enqvist et al. (2014) who found support for CCC, INV

and AR becoming increasingly important in economic downturns. However, there are

indications that INV are increasingly important in economic downturns while AR and AP are

increasingly important in economic booms. These indications are consistent for both ROA

and GOI. Most importantly, rejecting hypothesis 5-8 indicates that the importance of working

capital management do not vary with economic fluctuations in the sample. This can be viewed

from several aspects. Firstly, economic fluctuations might not affect the relationship between

working capital efficiency and firm profitability. Secondly, the way to measure the impact of

economic fluctuations fails to capture economic fluctuations. There may be other ways to

capture economic fluctuations that can provide better insights into how economic fluctuations

affect working capital management. Thirdly, different economic crises have different origins

and different effects on different firms (Reinhart & Rogoff 2010 p. 3-5). This might make it

hard to capture the impacts of economic fluctuations in general terms. Fourthly, there might

be contextual differences between Finland and Sweden, why the generalization of Enqvist et

al. (2014) cannot be supported for the Nordic region as a whole. In addition, the time period

differs between the studies, this might enhance the differences in economic environments

46

between the two studies. Lastly, in this thesis financial firms have been included. This might

also enhance the differences between this thesis and Enqvist et al. (2014).

Filbeck and Krueger (2005) highlighted interest rates, innovation and competition as

important determinants of working capital. According to Suomen Pankki (2017) and

Riksbanken (2017) there are considerable differences in interest rates during the period 1990-

2008 in Finland and 2000-2015 in Sweden. Even though the time periods overlap, the trend in

interest rates is downwards from the 1990s, accelerating after the financial crisis 2007-2008.

It is not unlikely that this might have an impact on the importance of working capital

efficiency. Cagle et al. (2013) discuss that CCC does not fully consider current liabilities, as

an effect interest among other factors is not fully captured. However, the lower the costs of

external financing, the less important the relationship between working capital efficiency and

firm profitability appears. This is in line with the reasoning of Filbeck and Krueger (2005).

The control variables used have all been shown to impact firm profitability (Enqvist et al.

2014). This was shown in the regression analysis in this thesis too. However, there were

differences between the results for the regression models using ROA and the ones using GOI.

More precisely, CR was shown to have a significant positive relationship with ROA but an

insignificant and varied relationship with GOI. Wang (2002) and Eljelly (2004) argued that

the way working capital efficiency affects firm profitability is through more efficient firm

liquidity. This result indicates that firms can improve their profitability by increasing their

margin of liquidity. However, this was only found for ROA. This is in line with Enqvist et al.

(2014). SIZE was found to be positively associated with firm profitability for both ROA and

GOI. This indicates that bigger firms are more profitable. This is in contradiction to Enqvist et

al. (2014) who found that bigger firms were less profitable. DEBT was found to have a

negative relationship with firm profitability for both ROA and GOI. This indicates that firms

with higher internal financing are more profitable. This is in line with Enqvist et al. (2014).

These results indicate that CR, SIZE and DEBT affect the relationship between working

capital efficiency and firm profitability. However, in comparison to Enqvist et al. (2014),

there is a discrepancy in regards to the effects of CR. Shin and Soenen (1998) found a positive

relationship between CR and firm profitability and this corresponds to other studies (Wang

2002; Eljelly 2004; Ebben & Johnson 2011). Ebben and Johnson (2011) concluded that firms

can improve both profitability and liquidity by shortening their CCC. An interpretation of this

is that firms can increase returns and reduce risk at the same time. The findings of this thesis

47

indicate that this might be the case. SIZE has consistently been associated with higher firm

profitability in previous research (Deloof 2003; Lazaridis & Tryfonidis 2006), why the

findings of Enqvist et al. (2014) are somewhat surprising. DEBT has been found to have a

negative relationship with firm profitability in previous studies (Deloof 2003; Lazaridis &

Tryfonidis 2006). This makes economic sense, as higher external financing should imply

higher costs and thus lower profitability. This has been discussed in general terms as well as

in regards to the economic environment (Einarsson & Marquis 2001; Braun & Larrain 2005;

Filbeck & Krueger 2005).

5.1.1 Descriptive statistics and correlations

In order to widen the discussion above, the regression analysis is complemented with

descriptive statistics and correlations. The characteristics of the variables of the sample do not

vary considerably between the full time period, economic downturns and economic booms

(see 3.5.3 for full sample and appendix C for economic downturns and economic booms).

While this does not necessarily affect the strength of the regression models, it still gives

valuable insights into differences between the studied time periods. Most obvious is the

higher ROA and GOI in economic booms than for the entire time period and particularly in

economic downturns. In addition, CCC and INV are generally lower in economic booms than

for the full time period and in economic downturns. One reason behind this might be that with

higher economic activity, firm performance increases overall. However, the strength of the

relationship between working capital efficiency and firm profitability does not change in this

direction. An interpretation of this is that over time, working capital efficiency, as measured

by CCC, does not vary considerably in the studied time period. It is notable that among the

sampled firms in this study, the average CCC is relatively consistent over time. This is in

contrast to Filbeck and Krueger (2005) who concluded that working capital management will

change over time. Correlation is not causality, but the correlations in the sample are generally

higher for GOI than for ROA. In addition, there are indications that the correlation is higher in

economic downturns than in economic booms (see table 7 under 3.5.4).

5.2 Contributions to research

This thesis contributes to the research of working capital efficiency and firm profitability in

two ways. Firstly, it contributes to the empirical body of knowledge about the relationship

between working capital efficiency and firm profitability. Secondly, it contributes with

insights into how this relationship is affected by economic fluctuations.

48

Firstly, previous research presents a strong case for a negative relationship between CCC and

firm profitability. This thesis contributes further to strengthen this position. In regards to INV

and AR, this thesis has found evidence that INV and AR are negatively associated with firm

profitability. This has been confirmed by previous research (Deloof 2003) and is theoretically

anchored (Jose et al. 1996) but has been difficult to show in other studies (e.g. Gill et al.

2010). In regards to AP, the findings of this thesis increase the ambiguity found in previous

studies (e.g. Deloof 2003; Garcia-Teruel & Martinez-Solano 2007; Mathuva 2010). This

ambiguity constitutes good reason for further investigation of AP. Furthermore, this thesis

fills a gap in the Swedish context. Yazdanfar and Öhman (2014) studied Swedish SMEs 2008-

2011 and found a negative relationship between CCC and firm profitability. This thesis

supports their findings and contributes to the previous literature by adding findings from the

largest listed Swedish firms. In addition, no previous study has been conducted on the

individual components of CCC in the Swedish context. As such, this thesis provides a starting

point for further investigation of INV, AR and AP and their relationships to firm profitability

among Swedish firms.

Secondly, Enqvist et al. (2014) suggested that their findings would be generalizable to the

Nordic region as a whole. The findings of this thesis do not support this position. While

Enqvist et al. (2014) found that the importance of CCC, INV and AR increased in economic

downturns, this thesis did not find any impact from economic fluctuations on the relationship

between working capital efficiency and firm profitability. These differences may depend on

several factors but it cannot be ruled out that economic fluctuations might not impact the

relationship between working capital efficiency and firm profitability. This too requires

further investigation.

5.3 Limitations of this thesis

This thesis is bound to its context and limited by uncertainties derived from the choice of

approach. By addressing these uncertainties, their impact on the results of the thesis should be

both understood and minimized. First it must be noted that this thesis is a replication study in

a long line of research examining the relationship between working capital efficiency and firm

profitability. This thesis does not deviate from the research tradition. Similar concerns have

been discussed in previous research (e.g. Deloof 2003; Garcia-Teruel & Martinez-Solano

2007).

49

Firstly, the use of secondary data may contain errors that distort the results. While the

sampled firms are subject to the same laws and regulations, accounting practices between

firms still exist. In addition, the data sample contained a high amount of missing and invalid

values. It is probable that the Thomson Reuters Datastream is incomplete to the extent that

this affects the results. In addition, firm-year observations are diminishing from year 2015

down to year 2000 because the database only contains data from active firms. For this reason,

firms that no longer exist, have been acquired by other firms, have merged with other firms or

are no longer publicly traded on the Stockholm Stock Exchange are not in the database. This

presents a possible survival bias in the data sample. These uncertainties have been controlled

for in several ways. Firstly, firm observations with missing, invalid or unrealistic values have

been excluded. Secondly, firm, year and industry have been controlled for in all regression

models. Thirdly, the regression models have been tested for the existence of factors that can

distort the statistical data analysis. Fourthly, several robustness checks were conducted. In

addition, the years studied were chosen in order to balance the survival bias and a satisfying

amount of years. In the best of worlds another database would have been used with the

possibility to extend the years studied and include all the firms that existed these particular

years.

Working capital management may be firm-specific to the extent that the straight-forward

theoretical framework may be applied to certain firms, certain types of firms or certain

industries. In this thesis, all listed firms have been included. Previous research presents a case

for working capital and working capital management being more important in certain

contexts. For this reason, it may be fruitful to consider working capital management in more

specific contexts.

5.4 Replication as a scientific method

This thesis shows the importance of replication studies in business research. While the results

of this thesis do not confirm the findings of Enqvist et al. (2014), it does give support for

replication as a scientific method. Mittelsteadt and Zorn (1984) mean that what cannot be

replicated is not worth knowing, and suggest that disconfirmations propose that new

approaches must be tested. The findings of this thesis suggest that alternative approaches to

measure the impacts of economic fluctuations on the relationship between working capital

efficiency and firm profitability should be tested. It also highlights a central problem with

replication studies. Firstly, the argument that neither business researchers nor business

50

practitioners should base decision-making on stand-alone studies appears valid. This thesis

questions the strength of the arguments of Enqvist et al. (2014) and ultimately the

implications for business researchers, business practitioners and policymaker. Secondly,

putting other researchers’ work under increased scrutiny also reveals problems with

replicability. In this case, one must consider how much detail studies in general can omit in

regards to methodology and results. It presents an interesting case, because it highlights a

discussion of how much information research articles should contain in regards to

methodology and results in particular (Bryman & Bell 2015 p. 50). Not being able to fully

comprehend how a study has been conducted, it is impossible to fully understand it and

ultimately replicate it. Thirdly, the implications given by researchers might be affected by the

context of the research. In this case, the scientific journal Research in International Business

and Finance is specialized towards alternative perspectives with articles giving policy

implications (Elsevier 2017). This certainly affects the implications formulated in the article.

Enqvist et al. (2014) proposes that policymakers take the working capital of firms into

account and provide them with liquidity in wake of crisis. This is a far reaching suggestion

from an article studying something that, in a vast literature review, can be perceived as a

matter of internal decision-making, resulting in profitability enhancements for individual

firms. In addition, when not studying business cycles it is hazardous to give policy

implications based on business cycles.

51

6. Conclusion

This thesis has examined the relationship between working capital efficiency and firm

profitability, and how this relationship is affected by economic fluctuations during the years

2000-2015 among listed Swedish firms. The findings of this thesis contribute to business

research in two ways. Firstly, it examines the relationship between working capital efficiency

and firm profitability. Secondly, it examines how this relationship is affected by economic

fluctuations. Together with previous research, this thesis provides evidence that firms can

enhance their profitability through improvements of their working capital efficiency. More

specifically, firm profitability is enhanced through shortening of the CCC and lower AR. In

addition, there are indications that INV has a negative relationship with firm profitability.

However, AP is two-edged and it appears that the effects on firm profitability are ambiguous.

Further, the findings of this thesis do not support that the relationship between working capital

efficiency and firm profitability is affected by economic fluctuations. In this thesis, it is

suggested that working capital management holds the potential to enhance firm profitability

regardless of the economic environment. For this reason, it is imperative to include working

capital management in a broader business scope on a continuous basis.

6.1 Suggestions for future research

In this thesis, a number of gaps in the literature on the relationship between working capital

efficiency and firm profitability have been identified. Increased knowledge about these

specific aspects would increase the understanding of the effects of working capital efficiency

on firm profitability. Firstly, it appears evident that firms can enhance their profitability by

shortening their CCC. However, the impact of the individual components of CCC needs to be

investigated further. Secondly, the effects of AP appear to be a special case. This needs to be

investigated further to better align theory and empirical findings. Thirdly, more research is

encouraged in the Swedish context in order to create a better understanding of specific

Swedish conditions. Fourthly, it has been suggested that working capital is firm-specific,

industry-specific and country-specific. However, there is a lack of research in more specific

contexts. Therefore, valuable insights can be gained by studying different aspects of working

capital management in more specific contexts. Among these are firms’ relationship with

customers and suppliers and how these non-financial values can affect working capital

management in business practice. Fifthly, while there was no support for the assumption that

economic fluctuations will affect the relationship between working capital efficiency and firm

52

profitability in this thesis, it is still an under-researched area. We encourage more research to

be conducted in order to better understand if and how economic fluctuations affect working

capital management.

53

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Appendices

Appendix A: Previous research

A.1 Cash conversion cycle and firm profitability

Table A: Previous research: Cash conversion cycle and firm profitability

Authors Title Context &

Sample

Profitability

measure

Findings Year

Yasdanfar &

Öhman (2014)

The impact of cash

conversion cycle on

firm profitability: An

empirical study based

on Swedish data

Sweden, SMEs ROA CCC has a

significant negative

relationship with firm

profitability

2008-

2011

Enqvist, Graham

& Nikkinen

(2014)

The impact of

working capital

management on

firm profitability

in different business

cycles: Evidence from

Finland

Finland, non-

financial listed

firms

ROA,

GOI

CCC has a

significant negative

relationship with firm

profitability

1990-

2008

Ebben &

Johnson (2011)

Cash Conversion Cycle

Management in Small

Firms: Relationships

with Liquidity, Invested

Capital, and Firm

Performance

USA, small

manufacturing

& retail firms

Asset

turnover,

ROIC

CCC has a negative

relationship with

liquidity and firm

profitability

2002-

2004

Sharma & Kumar

(2011)

Effect of Working

Capital Management on

Firm Profitability:

Empirical evidence

from India

India, non-

financial listed

firms

ROA CCC is significant

positive correlated

with firm profitability

2000-

2008

Mathuva (2010)

Falope & Ajilore

(2009)

The Influence of

Working Capital

Management

Components on

Corporate Profitability:

A Survey on Kenyan

Listed Firms

Working Capital

Management and

Corporate Profitability:

Evidence from Panel

Data Analysis of

Selected Quoted

Companies in Nigeria.

Kenya, listed

firms on Nairobi

Stock exchange

Nigeria, non-

financial firms

Net

operating

profit

ROA

CCC has a significant

negative relationship

with firm profitability

CCC has a

significant negative

relationship with firm

profitability

1993-

2008

1996-

2005

Garcia-Teruel &

Martinez-Solano

(2007)

Effects of working

capital management on

SME profitability

Spain, listed

SMEs

ROA

CCC has a significant

negative relationship

with firm profitability

1996-

2002

Lazaridis &

Tryfonidis

(2006)

Relationship between

working capital

management and

Greece, listed

firms

Gross

operating

profit

CCC has a significant

negative relationship

with firm profitability

2001-

2004

59

profitability of listed

companies in the

Athens stock exchange

Eljelly (2004) Liquidity - profitability

tradeoff: An empirical

investigation in an

emerging market

Saudi Arabia,

listed firms

Net

operating

income

CCC has a significant

negative relationship

with firm profitability

1996-

2000

Deloof (2003) Does Working Capital

Management Affect

Profitability of Belgian

Firms?

Belgium, non-

financial listed

firms

GOI CCC has a significant

negative relationship

with firm profitability

1992-

1996

Wang (2002) Liquidity management,

operating performance,

and corporate value:

evidence from Japan

and Taiwan

Japan &

Taiwan, listed

firms

ROA,

Return on

equity

CCC has a negative

relationship with firm

profitability

1985-

1996

Lyroudi &

Lazaridis (2000)

The cash conversion

cycle and liquidity

analysis of the food

industry in Greece

Greece, food

industry

ROA

Net profit

margin

Significant positive

relationship between

CCC and liquidity.

CCC was positively

related to firm

profitability

1997

Shin & Soenen

(1998)

Efficiency of Working

Capital Management

and Corporate

Profitability

USA, non-

financial firms

Operating

income plus

depreciation

related to

total asset

Negative relationship

between the net trade

cycle and firm

profitability

1975-

1994

Jose, Lancaster

& Stevens (1996)

Corporate returns and

Cash conversion cycle

USA, firms

among seven

industries

ROA,

Return on

equity

CCC has a significant

negative relationship

with firm profitability

1974-

1993

60

A.2 Days of inventory, days of accounts receivables, days of accounts payables and firm

profitability

Table B: Previous research: days of inventory, days of accounts receivables, days of accounts payables and

profitability

Authors Title Context

&

Sample

Profitability

measure

Findings

(INV)

Findings

(AR)

Findings

(AP)

Year

Enqvist,

Graham &

Nikkinen

(2014)

The impact

of working

capital

management

on firm

profitability

in different

business

cycles:

Evidence

from Finland

Finland,

non-

financial

listed

firms

ROA,

GOI

Negative

relationship

between

INV and

firm

profitability

Negative

relationship

between

AR and

firm

profitability

Negative

relationship

between

AP and

firm

profitability

1990-2008

Sharma &

Kumar

(2011)

Effect of

Working

Capital

Management

on Firm

Profitability:

Empirical

evidence

from India

India,

non-

financial

listed

firms

ROA Negative

relationship

between

INV and

firm

profitability

Positive

relationship

between

AR and

firm

profitability

Negative

relationship

between

AP and

firm

profitability

2000-2008

Mathuva

(2010)

Falope &

Ajilore

(2009)

The

Influence of

Working

Capital

Management

Components

on Corporate

Profitability:

A Survey on

Kenyan

Listed Firms

Working

Capital

Management

and

Corporate

Profitability:

Evidence

from Panel

Data

Analysis of

Selected

Quoted

Companies

in Nigeria.

Kenya,

listed

firms on

Nairobi

Stock

exchange

Nigeria,

non-

financial

firms

Net

operating

profit

ROA

Positive

relationship

between

INV and

firm

profitability

Negative

relationship

between

INV and

firm

profitability

Positive

relationship

between

AR and

firm

profitability

Negative

relationship

between

AR and

firm

profitability

Positive

relationship

between

AP and

firm

profitability

Negative

relationship

between

AP and

firm

profitability

1993-2008

1996-2005

Garcia-

Teruel &

Effects of

working

Spain,

listed

ROA Significant

negative

1996-2002

61

Martinez-

Solano

(2007)

capital

management

on SME

profitability

SMEs relationship

between

INV and

firm

profitability

Lazaridis

&

Tryfonidis

(2006)

Relationship

between

working

capital

management

and

profitability

of listed

companies in

the Athens

stock

exchange

Greece,

listed

firms

Gross

operating

profit

Significant

negative

relationship

between

INV and

firm

profitability

Significant

negative

relationship

between

AR and

firm

profitability

Significant

negative

relationship

between

AP and

firm

profitability

2001-2004

Deloof

(2003)

Does

Working

Capital

Management

Affect

Profitability

of Belgian

Firms?

Belgium,

non-

financial

listed

firms

GOI Significant

negative

relationship

between

INV and

firm

profitability

Significant

negative

relationship

between

AR and

firm

profitability

1992-1996

62

Appendix B: Variable summarization and formulas

Table C: Summarization and formulas for the dependent, independent and control variables

Variable Abbreviation Formula

Dependent

Return on asset ROA Net income

Total assets

Gross operating income GOI Revenue-Cost of goods sold

Total assets-Financial assets

Independent

Cash conversion cycle CCC Days of inventory +Days of accounts receivables -

Days of accounts payables

Days of inventory INV 365 (

Inventory

Cost of goods sold )

Days of accounts receivables AR 365 (

Accounts receivables

Revenue )

Days of accounts payables AP 365 (

Accounts payables

Cost of goods sold )

Dummy

Economic downturns D1 Categorization of economic downturns

Economic booms D2 Categorization of economic booms

Control

Current ratio CR Current assets

Current liabilities

Firm size SIZE ln(Sales)

Firm debt ratio DEBT Short term loans + Long term loans

Total assets

Year YEAR Control for firm and year effects

Industry INDUSTRY Control for industry effects

63

Appendix C: Descriptive statistics

The descriptive statistics of the different economic states used in this thesis are here

presented. Economic downturns and economic booms are categorized as the five years with

the lowest respectively the highest annual GDP growth during the 16-year period of the study.

To understand the features and characteristics of the variables of the sample in economic

downturns respectively economic booms, the descriptive statistics present the mean, standard

deviation and minimum and maximum values. Table D and table E below present the

descriptive statistics for economic downturn and economic booms respectively.

Table D: Descriptive statistics economic downturns

Variable Mean SD Min Max

CCC 88.617 78.817 -270.756 596.011

INV 71.870 66.680 0 414.079

AR 71.038 41.346 1.234 467.742

AP 54.291 48.317 0 383.468

ROA 0.024 0.1485 -1.143 0.731

GOI 0.318 0.294 -0.869 3.100

CR 1.963 1.781 0.234 19.810

SIZE 14.458 2.095 7.454 19.531

DEBT 0.477 0.198 0.007 1.408 Notes: Descriptive statistics for the sampled variables for the years 2001, 2008, 2009, 2012 and 2013, for a total of number of

847 observations.

Table E: Descriptive statistics economic booms

Variable Mean SD Min Max

CCC 82.806 65.999 -299.582 429.482

INV 65.908 58.930 0 411.897

AR 71.213 37.753 1.456 345.452

AP 54.315 48.158 0 393.451

ROA 0.036 0.167 -1.180 0.511

GOI 0.332 0.349 -0.715 4.662

CR 2.054 1.999 0.143 26.633

SIZE 15.477 2.135 7.147 19.560

DEBT 0.461 0.186 0.025 1.021 Notes: Descriptive statistics for the sampled variables for the years 2000, 2004, 2006, 2010 and 2015 for a total of number of

779 observations.

64

Appendix D: Statistical testing

D.1 Extreme values and outliers

The original sample contained a number of extreme values and missing values. Those values

interrupt and make the assumption testing in the OLS regression misleading. Therefore, the

data has been screened and cleaned from these values. The original sample contained 5,200

firm-year observations Firstly, missing values were removed leaving a sample of 2,667 firm-

year observations. Secondly, in order to identify these outliers and extreme values the data has

been checked manually using descriptive statistics and boxplots to identify which values are

categorized as extreme values and outliers in the data (Pevalin & Robson 2009 p. 289).

Extreme values and outliers have been trimmed and deleted manually for both dependent and

independent variables. 77 observations were removed from the sample. The final sample

represents 49.8% of the total original sample. The figure below illustrates boxplots of the

variables after they have been trimmed. Notably, some extreme values and outliers were kept

in the sample since these are plausible from a theoretical point of view.

-20

24

6

GO

I

0

100

200

300

400

500

AR

0

100

200

300

400

AP

-400

-200

0

200

400

600

CC

C

0

100

200

300

400

INV

65

D.2 Linearity test

OLS regression requires that the association between the dependent variable and the

independent variables are linear (Pevalin & Robson 2009 p. 329-330). The figures below

illustrate that there is a linear relationship between the dependent variable and independent

variables.

66

D.3 Normality test

OLS regression requires that the residuals are normally distributed (Pevalin & Robson 2009 p.

288-295). The residuals are the differences between the predicted and the actual values for

each case. In all regression models, the residuals are normally distributed with some kurtosis.

The first four histogram shows the residuals for the regression models using ROA and the

four remaining represent the residuals for the regression models of using GOI. In addition, the

standardized normal probability (P-P) plots illustrates that there is some variation for the

residuals. The variation and kurtosis tendency indicates that there are some violations in the

normality distribution for the residuals, this because there are outliers in the sample (Pevalin

& Robson 2009 p. 299).

01

23

4

Den

sity

-1 -.5 0 .5 1Residuals

01

23

45

Den

sity

-1 -.5 0 .5 1Residuals

01

23

45

Den

sity

-1 -.5 0 .5 1Residuals

01

23

45

Den

sity

-1 -.5 0 .5 1Residuals

67

0.5

11

.52

Den

sity

-1 0 1 2 3 4Residuals

0.5

11

.52

Den

sity

-1 0 1 2 3 4Residuals

0.5

11

.52

Den

sity

-1 0 1 2 3 4Residuals

0.5

11

.52

Den

sity

-1 0 1 2 3 4Residuals

0.0

00

.25

0.5

00

.75

1.0

0

Norm

al F

[(re

s-m

)/s]

0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)

0.0

00

.25

0.5

00

.75

1.0

0

Norm

al F

[(re

s-m

)/s]

0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)

0.0

00

.25

0.5

00

.75

1.0

0

Norm

al F

[(re

s-m

)/s]

0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)

0.0

00

.25

0.5

00

.75

1.0

0

Norm

al F

[(re

s-m

)/s]

0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)

68

0.0

00

.25

0.5

00

.75

1.0

0

Norm

al F

[(re

s-m

)/s]

0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)

0.0

00

.25

0.5

00

.75

1.0

0

Norm

al F

[(re

s-m

)/s]

0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)

0.0

00

.25

0.5

00

.75

1.0

0

Norm

al F

[(re

s-m

)/s]

0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)

0.0

00

.25

0.5

00

.75

1.0

0

Norm

al F

[(re

s-m

)/s]

0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)

69

D.4 Homoscedasticity

OLS regression requires that the variance of the residuals is constant, that is showing

homoscedasticity. If the variance of the residuals is not constant, that is showing

heteroscedasticity, the results will be impacted (Pevalin & Robson 2009 p. 288-295). To test

for homoscedasticity, scatterplots have been checked for all the regression models. The

scatterplots below illustrate each of the regression models that have been tested. As can be

illustrated, the assumption of homoscedasticity is present in each case.

-1-.

50

.51

Resid

uals

-.4 -.2 0 .2 .4Fitted values

-1-.

50

.51

Resid

uals

-.4 -.2 0 .2 .4Fitted values

-1-.

50

.51

Resid

uals

-.4 -.2 0 .2 .4Fitted values

-1-.

50

.51

Resid

uals

-.4 -.2 0 .2 .4Fitted values

70

-10

12

34

Resid

uals

0 .2 .4 .6Fitted values

-10

12

34

Resid

uals

0 .2 .4 .6Fitted values

-10

12

34

Resid

uals

-.4 -.2 0 .2 .4 .6Fitted values

-10

12

34

Resid

uals

0 .2 .4 .6Fitted values

71

D.5 Multicollinearity

OLS regression requires that the independent variables are not highly correlated (Pevalin &

Robson 2009 p. 288-295). Multicollinearity means that there is correlation between the

independent variables. Multicollinearity is problematic because it increases the variance of the

regression coefficients, thus harming the regression models. Multicollinearity has been tested

for through the VIF-test. In line with, Pevalin and Robson (2009 p. 302) and previous

research (e.g. Lazaridis & Tryfonidis 2006), VIF values above 10 and values under 0.1

indicates multicollinearity. As shown in the tables below, the VIF values for the independent

variables are above 0.1 and under 10.

Table F: Model (CCC): ROA = β0 + β1 CCCti + β2 D1 + β3 D2 + β4 (D1*CCCti) + β5 (D2*CCCti) + β6 CRti +

β7 SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti

Variable VIF

CCC 2.70

CR 1.56

SIZE 1.24

DEBT 1.51

(D1*CCC) 3.64

(D2*CCC) 3.44

Table G: Model (INV): ROA = β0 + β1 INVti + β2 D1 + β3 D2 + β4 (D1*INVti) + β5 (D2*INVti) + β6 CRti + β7

SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti

Variable VIF

INV 2.62

CR 1.53

SIZE 1.23

DEBT 1.53

(D1*INV) 3.32

(D2*INV) 3.05

Table H: Model (AR): ROA = β0 + β1 ARti + β2 D1 + β3 D2 + β4 (D1*ARti) + β5 (D2*ARti) + β6 CRti + β7

SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti

Variable VIF

AR 2.43

CR 1.54

SIZE 1.29

DEBT 1.50

(D1*AR) 5.20

(D2*AR) 5.54

Table I: Model (AP): ROA = β0 + β1 APti + β2 D1 + β3 D2 + β4 (D1*APti) + β5 (D2*APti) + β6 CRti + β7

SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti

Variable VIF

AP 5.68

CR 1.51

SIZE 1.24

DEBT 1.50

(D1*AP) 3.91

(D2*AP) 3.85

72

Table J: Model (CCC): GOI = β0 + β1 CCCti + β2 D1 + β3 D2 + β4 (D1*CCCti) + β5 (D2*CCCti) + β6 CRti + β7

SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti

Variable VIF

CCC 2.70

CR 1.56

SIZE 1.24

DEBT 1.51

(D1*CCC) 3.64

(D2*CCC) 3.44

Table K: Model (INV): GOI = β0 + β1 INVti + β2 D1 + β3 D2 + β4 (D1*INVti) + β5 (D2*INVti) + β6 CRti + β7

SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti

Variable VIF

INV 2.62

CR 1.53

SIZE 1.25

DEBT 1.53

(D1*INV) 3.32

(D2*INV) 3.05

Table L: Model (AR): GOI = β0 + β1 ARti + β2 D1 + β3 D2 + β4 (D1*ARti) + β5 (D2*ARti) + β6 CRti + β7

SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti

Variable VIF

AR 2.43

CR 1.54

SIZE 1.29

DEBT 1.50

(D1*AR) 3.20

(D2*AR) 3.34

Table M: Model (AP): GOI = β0 + β1 APti + β2 D1 + β3 D2 + β4 (D1*APti) + β5 (D2*APti) + β6 CRti + β7

SIZEti + β8 DEBTti + β9 YEARt + β10 INDUSTRYi + εti

Variable VIF

AP 5.68

CR 1.51

SIZE 1.24

DEBT 1.30

(D1*AP) 3.91

(D2*AP) 3.85